ijciis may 2010 vol. 1 no. 3

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International Journal of

Computational Intelligence and

Information SecurityISSN: 1837-7823

May 2010 IssueVol. 1 No. 3

IJCIIS Publication

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International Journal of Computational Intelligence and Information Security Vo. 1 No. 3, May 2010

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IJCIIS Editor and Publisher

P KulkarniPublishers Address:5 Belmar Crescent, CanadianVictoria, AustraliaPhone: +61 3 5330 3647E-mail Address: [email protected]

Publishing Date: May 30, 2010

Members of IJCIIS Editorial Board

Prof. A Govardhan, Jawaharlal Nehru Technological University, IndiaDr. Awadhesh Kumar Sharma, Madan Mohan Malviya Engineering College, IndiaProf. Deepankar Sharma, D. J. College of Engineering and Technology, IndiaDr. D. R. Prince Williams, Sohar College of Applied Sciences, OmanProf. Durgesh Kumar Mishra, Acropolis Institute of Technology and Research, IndiaDr. Imen Grida Ben Yahia, Telecom SudParis, FranceDr. Himanshu Aggarwal, Punjabi University, IndiaDr. Jagdish Lal Raheja, Central Electronics Engineering Research Institute, IndiaProf. Natarajan Meghanathan, Jackson State University, USADr. Oluwaseyitanfunmi Osunade, University of Ibadan, Nigeria

Dr. Ousmane Thiare, Gaston Berger University, SenegalDr. K. D. Verma, S. V. College of Postgraduate Studies and Research, IndiaProf. M. Thiyagarajan, Sastra University, IndiaProf. Nirmalendu Bikas Sinha, College of Engineering and Management, Kolaghat, IndiaDr. Rajesh Kumar, National University of Singapore, SingaporeDr. Raman Maini, University College of Engineering, Punjabi University, IndiaDr. Shahram Jamali, University of Mohaghegh Ardabili, IranProf. Sriman Narayana Iyengar, VIT University, IndiaDr. Sujisunadaram Sundaram, Anna University, IndiaDr. Sukumar Senthilkumar, National Institute of Technology, IndiaProf. V. Umakanta Sastry, Sreenidhi Institute of Science and Technology, India

Journal Website: https://sites.google.com/site/ijciisresearch/

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Contents

1. Fuzzy Mobility Estimation and Data Encryption with Interlacing and Location

Based Key Dependent Permutation (pages 4-12)

2. Arabic Syntax Impact on the English-Arabic, Arabic-English E-Translation

(pages 13-20)

3. An Extended Relational Algebra & Calculus for Fuzzy Databases (pages 21-31)

4. Artificial Intelligence and Security (pages 32-36)

5. A Dynamic and Professional Remote Mutual Authentication Scheme Using Smart

Cards (pages 37-44)

6. Computation Of Bio-Crypto Key From Multiple Biometric Modalities: Fusing

Minutiae With Iris Feature (pages 45-60)

7. Feature Selection For Microarray Datasets Using SVM & ANOVA (pages 61-66)

8. New Search for Video Compression (pages 67-73)

9. Memetic Differential Evolution Algorithm for Security Constrained Optimal

Power System Operation (pages 74-82)

10.An Improvement of RC4 Cipher Using Vigenre Cipher (pages 83-92)

11.Perceptual Effect And Block Mask Ratio (pages 93-101)

12.A Review on Security Issues in Mobile Ad-Hoc Networks (pages 102-110)

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Fuzzy Mobility Estimation and Data Encryption with Interlacing andLocation Based Key Dependent Permutation

Prasad Reddy. P.V.G.D*, K.R.Sudha2 , P Sanyasi Naidu 3

*Department of Computer Science and Systems Engineering, Andhra University,Visakhapatnam, India,

[email protected] of Electrical Engineering, Andhra University, Visakhapatnam, India,

[email protected]

Department of Computer Science and Systems Engineering, GITAM University,Visakhapatnam, [email protected],

Abstract

The transfer of information has been increasing exponentially since the last few decades. The wide spreaduse of WLAN (Wireless LAN) and the popularity of mobile devices increases the frequency of datatransmission among mobile users. In such scenario, a need for Secure Communication arises. Securecommunication is possible through encryption of data. A lot of encryption techniques have evolved overtime. However, most of the data encryption techniques are location-independent. Data encrypted with suchtechniques can be decrypted anywhere. The encryption technology cannot restrict the location of datadecryption. GPS-based encryption (or geo-encryption) is an innovative technique that uses GPS-technologyto encode location information into the encryption keys to provide location based security. The mobileclient transmits a target latitude/longitude coordinate and an LDEA key is obtained for data encryption to

information server. The client can only decrypt the ciphertext when the coordinate acquired form GPSreceiver matches with the target coordinate In order to minimize the frequency with which nodes advertisetheir movements we propose the fuzzy logic model so that the nodes can get alert to advertise theirmovements only when they are about to leave the decryption zone.. For improved security, a random key(R-key) is incorporated in addition to the LDEA key. The cipher text is obtained by interlacing and keydependent permutation.

Keywords Fuzzy logic, data security, location-based key, mobile security, random generator, permutation

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1. IntroductionThe dominant trend in telecommunications in recent years is towards mobile communication. The next generation networkwill extend todays voice-only mobile networks to multi-service networks, able to carry data and video services alongside thetraditional voice services. Wireless communication is the fastest growing segment of communication industry. Wireless

became a commercial success in early 1980s with the introduction of cellular systems. Today wireless has become a criticalbusiness tool and a part of everyday life in most developed countries.Applications of wireless range from common appliances that are used everyday, such as cordless phones, pagers, to highfrequency applications such as cellular phones. The widespread deployment of cellular phones based on the frequency reuse

principle has clearly indicated the need for mobility and convenience. The concept of mobility in application is not onlylimited to voice transfer over the wireless media, but also data transfer in the form of text , alpha numeric characters andimages which include the transfer of credit card information, financial details and other important documents.The basic goal of most cryptographic system is to transmit some data, termed the plaintext, in such a way that it cannot bedecoded by unauthorized agents. This is done by using a cryptographic key and algorithm to convert the plaintext intoencrypted data or ciphertext. Only authorized agents should be able to convert the ciphertext back to the plaintext.

GPS-based encryption (or geo-encryption) is an innovative technique that uses GPS-technology to encode locationinformation into the encryption keys to provide location based security. GPS-based encryption adds another layer of securityon top of existing encryption methods by restricting the decryption of a message to a particular location. It can be used with

both fixed and mobile.

The terms location-based encryption or geo-encryption are used to refer to any method of encryption in which theencrypted information, called ciphertext, can be decrypted only at a specified location. If, someone attempts to decrypt thedata at another location, the decryption process fails and reveals no details about the original plaintext information. Thedevice performing the decryption determines its location using some type of location sensor such as a GPS receiver.

Location-based encryption can be used to ensure that data cannot be decrypted outside a particular facility - forexample, the headquarters of a government agency or corporation or an individual's office or home. Alternatively, it may beused to confine access to a broad geographic region. Time as well as space constraints can be placed on the decryptionlocation.

Adding security to transmissions uses location-based encryption to limit the area inside which the intended recipient

can decrypt messages. The latitude/longitude coordinate of node B is used as the key for the data encryption in LDEA. Whenthe target coordinate is determined, using GPS receiver, for data encryption, the ciphertext can only be decrypted at theexpected location. A toleration distance(TD) is designed to overcome the inaccuracy and inconsistent problem of GPSreceiver. The sender can also determine the TD and the receiver can decrypt the ciphertext within the range of TD.Dennings model is effective when the sender of a message knows the recipients location L and the time that the recipientwill be there, and can be applied especially effectively in situations where the recipient remains stationary in a well-knownlocation.

2. The model

In the mobility model based on the geo-encryption technique[13] [14], in which both sender and receiver are mobile, withoutpre-planned itineraries, and can securely deliver their current locations to one another whenever necessary. In order to do this,each mobile node that will be receiving geo-encrypted messages needs to inform potential sender nodes about its intended

movement in order for a sender node to estimate the mobile nodes expected location at any point in time. This is done bysending information regarding the mobile nodes movement, which we call mobility parameters, to the sender via a sequenceof message exchanges.

3. Mobility parameters

Let A be a mobile Node (MN) and let B be a Base Station (BS) station in a network using Denning style geo-locking for anadded layer of security. In our model, the geo-locking function takes shape, time, velocity, direction, and two maneuverability

parameters. The shape parameters define an ellipse as the decryption zone. An ellipse is suitable for the shape of ourdecryption zone because it has a length and breadth, and when both are equal, the ellipse becomes a circle that providesuniform coverage in all directions. (A rectangle also has a length and breadth, but when both are equal, it forms a square, withnon-uniform coverage.) The time parameter specifies the period during which decryption is possible. When A is in motion, Bwill need to calculate a time parameter that represents a future time when A will actually be in the decryption zone when a

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geo-encrypted message arrives for decipherment at A. Fig. 1 shows the four mobility parameters that a mobile node uses toadvertise its movement information. The velocity parameter, V, describes the recipients speed. This is the average speed atwhich the recipient is expected to travel. Velocity (V) is determined from observing the distance traveled during a specifiedtime unit it is automatically calculated from recent movement, even if the user does not specify. The direction parameter, ,

describes the direction in which the recipient is traveling and is measured as the positive angle between the positive x-axisand the velocity vector on a Cartesian coordinate system. The first maneuverability parameter, , is an indication of howfrequently the moving recipient might need to change speeds while traveling to the new destination (how much leeway, interms of speed changes, that should be built into the size of the decryption zone). This speed maneuverability parameterinfluences the length of the ellipse-shaped decryption zone. The second maneuverability parameter, , defines how much themoving recipient might deviate from a straight line while traveling to a new destination.A mobile station must determine its own velocity and maneuverability parameters, based on its recent movement and anevaluation of the terrain in question, and communicate them to other stations for use in geo-locking messages back to themoving station.

Figure. 1. Diagram to illustrate the four mobility parameters.

The decryption zone only needs to be large enough for A to extract the geo-secured decryption key within the specified timeperiod, not for A to decrypt the accompanying message.

4. The model equations

Suppose the MN A starts at time t0 at a location whose longitude and latitude values are LA0(X0,Y0), which are assumed tobe initially known to BS B. This could be achieved, for example, by using the geo-encryption model in [13][14], or by anyother means. Periodically, node A collects GPS location satellite readings LAt (Xt,Yt) at time t with t = t1, t2, t3, . . . such thatti = t0 + i*r where r is a fixed time unit interval whose value is arbitrary but known. To define the decryption region for themobile node A, it is assumed that some initial values are available for the mobility parameters 0, 0, V0, and h0 at time t0.Given these initial values for the mobility parameters and LA0(X0,Y0) as the initial values for the center of the ellipse, thedecryption region for node A is defined initially. The line of movement makes an angle h0 with the positive direction of thelatitude..The parameters of the center of the decryption region constantly change with time but not the shape. The parameters of theshape of the region remain fixed and are only allowed to change when a predetermined fixed number n of time units has

passed. The center (CXt, CYt) of the decryption region at time t is given byLet ti=t0+i*rAt the ith time instant where r is a fixed value of timeCXt=X0+ti*V*CosCYt=Y0+ti*V*Cos

The velocity, angle and distance between MN and BS at any instant d are given by

21tt2

1tt

1tt

1tt1

21tt

21tt

CyCyCxCxd

CxCx

CyCyTan

r

CyCy

r

CxCxV

Thus, at any time node B needs only the initial parameters and the time value t to locate the center of the decryption region. Ifwe assume the region has the bivariate normal distribution with center (CXt, CYt) and if we adopt the 3-sigma rule [14] thenthe equations relating the shape parameters of the region with the maneuverability parameters are given by

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Sin)CY(6

1Cos)CX(

6

1CX tttx

Cos)CY(6

1Sin)CX(

6

1CY ttty

Hence at time t the decryption region is defined by:c

)CYY()CXX(2

)CYY()CXX()Y,X(R

2y

tt2x

tt2y

2tt

2x

2tt

tt

Where Cos and c is a constant determined from the values of and .

Figure2. Movement of the decryption region along a line.

5. Updating the mobility parameters

Each time the mobility parameters are estimated, the mobile node must decide whether or not to replace the old values of theparameters with the new values and whether or not to advertise them[14]. Typically, the old values are replaced with the newvalues and the updates are advertised only when they are significant, i.e., when the difference between the old and the newvalues of a parameter exceeds some predetermined threshold set by the mobile node. Otherwise, the old values are kept and

nothing advertised. In addition to the mobility parameters, the initial location parameters (X0,Y0) of the mobile node mustalso be updated once V and direction are found significant. This is because the geo-encryption process depends ondetermining the center (CXt,CYt). If at time t* a significant V or is detected then not only the four mobility parameters are

advertised but also the new value for t0 which is estimated by 0t

=t*. Given the values of V , , and t the recipient will use

Eq. (1) to estimate the updated initial location( 00Y,X

)

6. Mobility Updating using Fuzzy Logic

To update the parameters the fuzzy logic technique is used in the present paper. The fuzzified inputs are Velocity V , distance

d and angle . The decryption region is calculated and if it is less the pre described value, the encryption is done else the

mobile parameters are to be updated.The proposed model attempts to achieve the goal of keeping the locations secret from rivals, and permit the stations to be asmobile and maneuverable as possible.In order to minimize the frequency with which nodes advertise their movements and at the same time optimize the size of thedecryption zone, we propose the fuzzy logic model so that the nodes can get alert to advertise their movements only whenthey are about to leave the decryption zone.A mobile node may fall into one of three regions as shown in Figure 4. Fuzzy decryption zone is represented in Fig 5 whichshows gradual transition from region 1, 2 and 3Region 1 represents the advertisement-freezone, meaning that a mobile node will not advertise movement updates when they fall within this region although itconstantly updates them. Regions 1 and 2 together make up the decryption zone. In region 2, the mobile node is about toleave the decrypt zone and enters the non-decrypt zone of region 3. In this zone, the node needs to transmit its mobile

parameters updates if they are significant.

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They are declared significant when the updates in the mobility parameters exceed the parameters thresholds. Note that byrestricting advertisement of updates to region 2 we effectively reduce the frequency of advertisements

.Figure 3: Regions 1, 2, and 3 of decryption zone.

Figure 4: Fuzzy representation of Regions 1, 2, and 3 of decryption zone.

Figure 5: Input Membership function

The Fuzzy rules for the proposed model are

Vtheta

short Medium long

Short Region 1 Region 1 Region 2Medium Region 1 Region 2 Region 3Large Region 2 Region 3 Region 3

For d smallTable 1 : Fuzzy Rules for the proposed model

Similarly the rules can be written for the other two combinations of d that is d medium and d large to cover all possibleconditions .

Region 1

Region 3

Region 2

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7.Location Dependent Encryption algorithm:

The purpose of Location Dependent Encryption Algorithm(LDEA) is to include Latitude/Longitude coordinate in the data

encryption and thus to restrict the location of data encryption. A Tolerance distance (TD) is designed to overcome theinaccuracy and inconsistent problem of GPS receiver. When the target coordinate and latitude/longitude are given by thesender, an LDEA key is generated from the TD and Latitude/longitude. The random key generator issues a session key, calledR-key. Then the final key for encrypting the plain text is generated by using the key dependent permutation.

7.1Random number generator using quadruple vector:For the generation of the random numbers a quadruple vector is used[11][8][11][15][16]. The quadruple vector T is generatedfor 44 values i.e for 0-255 ASCII values.T=[0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 2 2.. 0 1 2 3 0 1 2 3 0 1 ..3]The recurrence matrix[1][2][3] [4]

100

011

010

A

is used to generate the random sequence for the 0-255 ASCII characters by multiplying s=[ A] *[T] and considering thevalues to mod 4. The random sequence generated using the formula [40 41 42]*s is generated.[11]

7.2Transforming Latitude/Longitude coordinates:The target coordinates can be determined by the sender or receiver. If it is determined by the sender, the sender can informthe receiver the physical location for data encryption. This can be communicated in a secured way such as a telephone. Afterthe receiver gets the target coordinates, the data can be transmitted to the receiver using the algorithm given below.The generation of the LDEA key and Final key is presented in the following section assuming the TD as 5m.The format ofthe coordinates acquired from the GPS receiver is WGS84(World Geodetic System 1984) defined in NMEA ( NationalMarine Electronics Association) specification. For example, E 12134.5971 means 121 and 34.5971 min east longitude. N2504.7314 means 25 and 4.7314 min north latitude. The Coordinates are multiplied by 10000 to be an integer. Then theinteger is divided by TD to avoid the coordinate inaccuracy. The values of 1m latitude and Longitude are correspondingly 5.4and 6[12]. In advance, one bit is put in front of the integral part of the above result . The bit is zero for east and south and 1

for west and north. The LDEA key is obtained as 2334719.Development of the cipher:

Consider a plain text represented by P which is represented in the formP=[Pij] where i=1to n and j=1 to n ---1Let the R-key matrix be defined byK=[Kij] where i=1 to n and j=1 to n ---2Let the cipher text be denoted by C=[ Cij] where i=1to n and j=1 to n corresponding to the plain text (1)For the sake of convenience the matrices P,K and C are represented asP=[p1 p2 pn2]K=[k1 k2 kn2]C=[c1 c2 cn2]

7.3Algorithm for generation of R-key and LDEA key:

Algorithm:Algorithm for Encryption:{read n,K,P,rPermute(P)For i=1 to n{

p=convert(P);X=p LDEA key

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Interlace(X)C1=Permute(X)}Write(C)

}Algorithm for Decryption:{read LDEA-key,R-key,n,C

for i=1 to n{X=permute(C)Interlace(X)

p= X LDEA keyP=convert(p)}Permute(P)write P;}

8. Illustration of the Cipher:

Encryption :The distance between every pair of points in the universe is negligible by virtue of communication facilities. Let us reacheach point in the sky. This is the wish of scientists.

ASCII equivalent is obtainedLDEA- key:2334719X=P xor LDEA keyAfter transposing and permuting with the keycipher text C is obtainedThe encryption are illustrated in Fig 6(a)-(b )

Decryption:From the cipher text after transposing and Permuting with the keyP=X xor LDEA key,the required plain text is obtained.The decryption are illustrated in Fig7(a)-(b)

Cryptanalysis:

If the latitude and longitude coordinate is simply used as the key for data encryption the strength is not strong enough. That isthe reason why a random key is incorporated into LDEA algorithm. The Cipher cannot be broken with known plain textattack as there is no direct relation between the plain text and the cipher text even if the longitude and latitude details areknown.It is noted that the key dependent permutation and inteterlacing plays an important role in displacing the binary bits at various

stages of iteration, and this induces enormous strength to the cipher.

Avalanche Effect:

With change in LDEA key from 2334719 to 2334718 . It is observed that there is a 177 bit change in the new cipher text.

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(a) (b)

Figure 6 Prototype for Encryption

(a) (b)Figure 7 Prototype for Decryption

9.Conclusions:

In present paper a cipher is developed using interlacing and the LDEA key dependent permutation as the primary concept.Moreover the mobility parameters are updated using fuzzy logic which avoids the frequent advertisement of the mobilityparameters. The cryptanalysis is discussed which indicates that the cipher is strong and cannot be broken by any cryptanalyticattack since this includes confusion at every stage which plays a vital role in strengthening the cipher.

10. Acknowledgements:

This work was supported by grants from the All India Council for Technical Education (AICTE) project under RPS Schemeunder file No. F.No.8023/BOR/RID/RPS-114/2008-09.

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11. References:

[1] K.R.Sudha, A.Chandra Sekhar and Prasad Reddy.P.V.G.D Cryptography protection of digital signals using someRecurrence relations IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.5, May 2007

pp 203-207[2] A.P. Stakhov, The golden matrices and a new kind of cryptography, Chaos, Soltions and Fractals 32 ( (2007)

pp11381146[3] A.P. Stakhov. The golden section and modern harmony mathematics. Applications of Fibonacci numbers, 7,Kluwer

Academic Publishers; (1998). pp39399.[4] A.P. Stakhov. The golden section in the measurement theory. Compute Math Appl; 17(1989):pp613638.[5] Whitfield Diffie And Martin E. Hellman, New Directions in Cryptography IEEE Transactions on Information Theory,

Vol. -22, No. 6, November 1976 ,pp 644-654[6] Whitfield Diffie and Martin E. Hellman Privacy and Authentication: An Introduction to Cryptography

PROCEEDINGS OF THE IEEE, VOL. 67, NO. 3, MARCH 1979,pp397-427[7] Tzong-Mou Wu One-to-one mapping matrix Applied Mathematics and Computation 169 (2005) 963970[8] A. V. N. Krishna, S. N. N. Pandit, A. Vinaya Babu A generalized scheme for data encryption technique using a

randomized matrix key Journal of Discrete Mathematical Sciences & Cryptography Vol. 10 (2007), No. 1, pp. 7381

[9] C. E. SHANNON Communication Theory of Secrecy Systems The material in this paper appeared in a confidentialreport A Mathematical Theory of Cryptography dated Sept.1, 1946, which has now been declassified.[10]E. Shannon, A Mathematical Theory of Communication, Bell System Technical Journal 27 (1948) 379423, 623656.[11]A. Chandra Sekhar , ,K.R.Sudha and Prasad Reddy.P.V.G.D Data Encryption Technique Using Random Number

Generator Granular Computing, 2007. GRC 2007. IEEE International Conference, on 2-4 Nov. 2007 Page(s):576 576[12]V. Tolety, Load Reduction in Ad Hoc Networks using Mobile Servers. Masters thesis, Colorado School of Mines, 1999.[13]L. Scott, D. Denning, Geo-encryption: Using GPS to Enhance Data Security, GPS World, April 1 2003.[14]Geo-encryption protocol for mobile networks A. Al-Fuqaha, O. Al-Ibrahim / Computer Communications 30 (2007)

25102517[15]PrasadReddy.P.V.G.D, K.R.Sudha and S.Krishna Rao Data Encryption technique using Location based key dependent

Permutation and circular rotation (IJCNS) International Journal of Computer and Network Security,Vol. 2, No. 3,March 2010 pp46-49

[16]Prasad Reddy.P.V.G.D, K.R.Sudha and S.Krishna Rao Rao Data Encryption technique using Location based key

dependent circular rotation Journal of Advanced Research in Computer Engineering, Vol. 4, No. 1, January-June 2010,pp. 27 30

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Arabic Syntax Impact on the English-Arabic, Arabic-English E-Translation

Hamsa H. Aljudi 1, B.B.Zaidan 2, A.A.Zaidan 2, Zahraa M.Hamad 1

1 Department of English Language Study / Faculty of Social Science and Humanities School ofLanguage Studies and Linguistics/ University kebangsaan Malaysia / Malaysia

2 Department of Electrical and Computer Engineering/ Faculty of Engineering, / Multimedia University /63100 Cyberjaya / Selangor Darul Ehsan / Malaysia

[email protected], [email protected], [email protected]

Abstract

Translation has been considered as one of the oldest arts brands, the first renowned translation has been found in Iraqwhen Babylonians translate the graphic writing to letter writing, currently there are many kind of electronic translators, mostwidely use for communication, learning systems, etc, in this paper the author will try to explain why E-translator couldntachieve the altitude of human translating, the author choice Arabic and English language as a case for his study, besides,Google translator will be a genuine example for E-translators, other point will be cover on this study; a comparative

background between Arabic and English syntax and its effect on the English-Arabic, English Arabic E-Translation ,furthermore this study will give examples from Google translation which stand for one of the best E-translation currently, last

but not least this study will suggest a new E-translation design overcome the current problems by using a huge Data base ofhuman translation and a intelligent system to analysis the Arabic words to improve the efficiency of the E-translators

Keyword: - Arabic Syntax, English Syntax, E-Translation, Google Translator

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1. Introduction

Computer technology has been used in translation for about five decades in the form of machine-assisted human translation, human-assisted machine translation and terminology databanks. Access to thelatter was not made during the translation of the text but performed before. Many translators could nothave access to them in particular if they were not working on-line. Some databanks were not accessible tothe translator on-line at all because they were limited to the production of a printed glossary in a particularsubject area. They offered access to technical terminology -- not common words. Further, they hadadvantages over traditional dictionaries as their terminology was always up-to-date and they containedmore entries [4].

Nowadays, this kind of E-translators has been widely used, in fact, great roles played on the studentlife, in a study done by Prof. Reima Saad Al-Jarf College of Languages and Translation, King SaudUniversity, under a title Electronic Dictionaries in Translation Classrooms in Saudi Arabia a survey hasdone on 178 students majoring in translation at the College of Languages and Translation and 10 translationand interpreting instructors were surveyed. It was found that 45% of the students use an electronic

dictionary (ED). 99% of those uses a general English-Arabic ED, 68% use an Arabic-English ED, 27% usean English-English ED and

only 2% use a specialized ED. The students gave 12 reasons for not using an ED in specializedtranslation courses. It was also found that 70% of the translation instructors do not allow students to use anED in class or test sessions [5].

2. Motivation

Over the past Ten years the U.S. government has financed an enormous effort to improve machinetranslation systems, so as to process more information from non-English sources. The governments

National Institute of Standards and Technology has conducted annual evaluations of such systems, manyfrom universities or other research groups. NIST avoids using terms like contest and winner. But in 2005,Googles system was the highest-ranked in all evaluations involving the two hard languages NIST used inits assessment: Arabic and Chinese. Last year, Google ranked highest in six out of eight tests.

In the next three years after the new generation for this translator, we did find a lot of enhancement hasbeen done on that area, in term of using several AI tools, even though the expert in the translation believethe problem on this translators is not technology problems, yet many challenge, not only the tool of AI oreven programming, it shows that most of the problem is the people who work on the E-translation could not

provide the syntax of real human translation in fact they make it as a mix of technology try to carry out agreat non human translators.

3. Background and Problem Discassion

Arabic has much richer morphology than English.

3.1 Arabic Noun

Arabic noun has two genders, feminine and masculine (male, female); three numbers, Singular, dual,and plural; and three grammatical cases, nominative, genitive, and accusative, A noun has the nominativecase when it is a subject; accusative when it is the object of a verb; and genitive when it is the object of aPreposition. The form of an Arabic noun is determined by its gender, number, and grammatical case [1].

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Fig1. Shows the Noun Tree for Arabic languageMost of the problems in the translating come from the poor of English language comparing to the

Arabic language in term of morphology.

3.2 Arabic Verbs

Arabic verbs have two tenses: perfect and imperfect. Perfect tense denotes actions completed, whileimperfect denotes uncompleted actions. The imperfect tense has four moods: indicative, subjective, jussive,and imperative.[3] Arabic verbs in perfect tense consist of a stem and a subject marker. The subject markerindicates the person, gender and number of the subject. The form of a verb in perfect tense can have subjectmarker and pronoun suffix. The form of a subject-marker is determined together by the person, gender, andnumber of the subject [1].

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Fig 2. Shows the Verb Tree for Arabic language

3.3 Arabic Adjective

Arabic adjective can also have many variants. When an adjective modifies a noun in a noun phrase,the adjective agrees with the noun in gender, number, case, and definiteness.

Fig 3. Shows the Adjective Features Tree for Arabic language

Notes

In the Arabic language there are some feature which is also represent a problem in the E-translationwhich are followed below:

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The definitive nouns are formed by attaching the Arabic article to the immediate front of thenouns Example: the boy in Arabic

As well as the definitive the preposition in Arabic also by attaching (by) and (to), is attached tothe front of a noun, often in front of the definitive article. Example: to the boys

Besides prefixes, a noun can also carry a suffix which is often a possessive pronoun. Example: tomy boys

Arabic, the conjunction word () (and) is often attached to the following word. For example, andher boy

As conclusion we have to type of attaching, prefix and suffix

3.4 Arabic has two kinds of plurals

Arabic has two kinds of plurals: sound plurals and broken plurals. The sound plurals are formed byadding plural suffixes to singular nouns.For example of sound plural, the word (teachers, masculine) is the plural form of

(teacher, masculine)Broken plurals are very common in Arabic. For example, the plural form of the noun ((boy

(boys)Moreover, often the subject-makers are suffixes, but sometimes a subject-marker can be a

combination of a prefix and a suffix, in addition to the subject-marker, a verb can also have a mood-marker.[1]

Table 1: Arabic Words Whose English Translations Contain the Headword Child or Children [1].

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4. Methodology

The new module carry the data mining principle which so-called AI, but we have seen clearly how itlook a weak whereby the people in the linguistic field pay no attention to this type of translators, however

in the same time we cannot forget a lot of benefits come from these translators such as time cost, easy use,helpful, cheap translating. The poor point in these translators is they depend only in the AI tool, thats whyit doesnt show the expecting result. Our module depends on the analysis of a huge human translating andsome algorithms of matching as showing below. We will depend in our module to the comparing betweensentences not words.

Fig 4. The New Module

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Fig 5. System Block Diagram

5. Examples of E-Translation

In this part the author use google translation to translate from Arabic to English and from English toArabic both of the translation has been giving wrongly.

Examples

Google translation Arabic-English

= Played a ball girl

The right translation is the girl play with ball

Google translation English-Arabic. The girl play with ball = .

The right translation is

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6. Conclusion

E-translation or (with one bottom translate unlimited text) is an awesome service nowadays, in which

you can translate long text with a second, however the expert on the translation say it is not real translationand non reliable translation, in fact they didnt prefer to use this kind of translators, even though we maynot forget the great roles provided from the e-translations, in this paper the author explain some of theArabic language feature which might be effect experimentally the e-translations, we also shows thechallenges of accurate the electronic translation to be act as a human translations, we also suggest a newmodule with a multi feature use the word and the sentence analyzing to come with an efficient translation,we will also make another study about the English language syntax to identify the rules of the new systemhabits.

ACKNOWLEDGEMENT

This work was supported in part by the University kebangsaan Malaysia and Multimedia University.The author would like to think in advance both of his partner; his supervisor for her unlimited support andZahraa M.Hamad as one of the entire worker on this project, the author would like also to think his brothers

bilal, aws for those help to provide this research paper

REFERENCES

[1] Aitao Chen, Fredric Gey , 2003, Building an Arabic Stemmer for Information Retrieval, School ofInformation Management and Systems, University of California at Berkeley, CA 94720-4600, USA.

[2] W. S. Cooper, A. Chen, and F. C. Gey. Full text retrieval based on probabilistic equations with coefficients fitted by logistic regression. In D. K. Harman, editor, The Second Text Retrieval Conference(TREC-2), pages 5766, March 1994.

[3] M. Zaidel D. Karp, Y. Schabes and D. Egedi. A freely available wide coverage morphological analyzer

for English, In Proceedings of COLING, 1992[4] Nirenburg, Sergei. (Ed.) (1987). Machine Translation: Theoretical and Methodological, Issues.

Cambridge, University Press.[5] Prof. Reima Saad Al-Jarf, Electronic Dictionaries in Translation Classrooms in Saudi Arabia,

College of Languages and Translation, King Saud University.

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An Extended Relational Algebra & Calculus for Fuzzy Databases

Awadhesh Kumar Sharma1, A. Goswami2, and D.K. Gupta21MMM Engineering College, Gorakhpur-273010, UP, India2Indian Institute of Technology, Kharagpur-721302, India

E-mail: [email protected]; [email protected],[email protected]

AbstractDesign of fuzzy databases requires several theoretical foundations, efficiency and ease of their uses.

The information obtained from fuzzy databases can be used in decision making and problem solving in theenvironment which involves uncertainty and imprecise, incomplete, or vague information that can be dealtwith approximate reasoning. Fuzzy data are important because real world data occurs with partial orincomplete knowledge associated with it. For example, if the temperature of a person suffering from fever

is 101.8

o

F then one may specify it to be around 102

o

F. Others may indicate the same by saying that theperson is suffering from high fever. All the above statements are relevant for answering queries related tothe condition of the person in terms of the state of his fever. To manipulate such information and a varietyof null values, the fuzzy relational data models are developed based on fuzzy set theory and possibilitytheory. Thus the fuzzy relational data models are rigorous schemes for incorporating fuzzy information inclassical relational databases and in operations of relational algebra. The basic definitions, concepts andnotations used in this paper are taken from fuzzy set theory.Keywords: Fuzzy Set Theory, Possibility Theory, Fuzzy Database, Relational Algebra.

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1. IntroductionFor most of the real world applications which involve uncertainty, fuzzy databases are developed based on

the relational data model. Thus, a lot of research has been carried out in extending the relational data model tofuzzy relational data model. A fuzzy database can be defined as a set of fuzzy relations which are treated in [15,8]. Buckles and Petry [2] proposed a structure to represent inexact information which differs from relationaldatabases in two respects. The first one is that the components of a tuple need not be single valued and the

second one is that for each set of domain values, we will need a similarity relation. The proposed structurepreserves two important properties of relational databases namely no two tuples have identical interpretations,and each relational operation has a unique result. Prade and Testamale [10] have given a generalization ofdatabase relational algebra for the treatment of incomplete or uncertain information in which the basicoperations of relational algebra such as union, intersection, cartesian product, projection and selection areextended in order to deal with partial information and vague queries. It has been shown that approximateequalities and inequalities modeled by fuzzy relations can also be taken in account in selection operation.Rundensteiner et al [11] introduced a new equality measure termed as resemblance relation.

This paper classifies the fuzzy relational data model into two categories Type-1 & Type-2 depending oncomplexity of attribute domains. In Type-1 the domains can only be fuzzy sets (or a classical set). Type-1 fuzzyrelations may be considered as first level extension of classical relations where impreciseness in associationsamong attribute values is captured. The Type-2 fuzzy relations provide further generalization by allowing the

attribute domains to be sets of fuzzy sets (or possibility distributions). The relational data model described herehas some similarities with the fuzzy relational data models considered by Buckles and Petry [2, 3], Baldwin [1],Pradey and Testimale [10], and Umano [13, 14].

2. Notations & Definitions

Definition 1. Let U be a universe of discourse. A set F is a fuzzy set of U if there is a membership

function : [0,1]F

U , which associates with each element u U a membership value ( )F

u in the

interval[0,1] . The membership value ( )F

u for each u U represents the grade of membership of the

element u in the fuzzy set F. F may be represented by { ( ) / | }F

F u u u U .

Definition 2. Let * 1 2 nU U U U be the cartesian product of n universes and 1 2, , , nA A A be fuzzy

set in 1 2, , , nU U U respectively. Then the cartesian product 1 2 nA A A is defined to be a fuzzy

subset (denoted byf

) of 1 2 nU U U , with

1 2 1 21 2 1 2

( , , , ) min ( ), ( ), , ( )n nA A A n A A A n

u u u u u u

Where , 1, 2, ,i i

u U i n . An n -ary fuzzy relation R in *U is a relation that is characterized by a n -

variate membership function ranging over

*

U , that is,

*

: [0,1]R U .

Example 1. Suppose there is a need to capture the set of intelligent students in a university. Let the attributeswhich identify the intelligence level of a student are its Name, SSN, Age and the Grade in universityexamination. The fuzzy relation namely, Intelligent-student-relation can be represented by the Table 1.

Table 1 Intelligent-student-relation Name SSN Age Grade

r

Jack 12345678 0.87/22 0.77/A 0.77Dave 98765432 0.93/33 0.88/B 0.88

Intelligent-Student-Relationf

r Name SSN Age Grade

Therefore, ( ,12345678, 0.87 / 22, 0.77 / ) 0.77r Jack A

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Similarly, ( ,98765432, 0.93 /33,0.88 / ) 0.88r

Dave A

Definition3. Given a fuzzy set A defined on Uand any number [0,1] , the -cut A , and the strong

-cut, A , are the crisp sets { | ( ) }A

A u u ; { | ( ) }

AA u u

.Any property generalized from classical set theory into the domain of fuzzy set theory that is preserved in

all -cuts for (0,1] in the classical sense, is called a cutworthy property. If it is preserved in all strong -cuts for [0,1] , it is called strong cutworthy property.

3. Fuzzy RelationsAn important tool for many applications of fuzzy set theory is the concepts of fuzzy relations and fuzzy

relational equations. Mathematically, an n -ary fuzzy relation r is defined as a fuzzy subset of the Cartesian-

product of some universes. Thus, given n universes 1 2, , , nU U U , fuzzy relation r is a fuzzy subset of

1 2 nU U U and is characterized by the n -variate membership function (Dubois et al [5], Kaufman [8],Zadeh [16]).

1 2: [0,1]r nU U U

While applying this definition to relational databases, it is necessary to provide appropriate interpretationfor the elements of , 1,2, ,i

U i n andr

. For this purpose, it is noted that a relational data model that can

support imprecise information, it is necessary to accommodate two types of impreciseness-namely, theimpreciseness in data values and impreciseness in the association among data values. As an example ofimpreciseness in data values, considerEmployee( Name, Salary) database, where salary of an employee, sayJohn, may be known to the extent that it lies in the range $ 20000 - $40000 or may be known as John has a

High-Salary. Similarly, as an example of impreciseness in the association among data values, let Likes(Student,Course) represent how much a student likes a particular course. Here the data values may be precisely known,but the degree to which a student, say - John, likes a course is imprecise. It is also not difficult to envisageexamples where both ambiguities in data values as well as impreciseness in the association among them arepresent.

The present treatment of fuzzy relational data model will try to adhere to the notations used in the classicalrelational database theory as far as possible. Thus a relation scheme R is a finite set of attribute names

1 2{ , , , }nA A A and will be denoted by 1 2( , , , )nR A A A or simply by R . Corresponding to each

attribute ,1i

A i n , there exists a set ( )i

dom A , called the domain ofi

A . However, unlike classical relations,

in the fuzzy relational data model, ( )i

dom A may be fuzzy set or even a set of fuzzy sets. Hence along with

each attributei

A , a seti

U is associated, called the universe of discourse for the domain values ofi

A .

Definition 4. A fuzzy relation ron a relation scheme 1 2( , , , )nR A A A is fuzzy subset of

1 2( ) ( ) ( )ndom A dom A dom A .

Depending on complexity of ( ), 1,2,i

dom A i n ,fuzzy relations are classified into two categories. In

type-1 fuzzy relations, ( )i

dom A can only be a fuzzy set (or a classical set). A type-1 fuzzy relation may be

considered as a first level extension of classical relations that will enable to capture the impreciseness in the

association among entities. The type- 2 fuzzy relations provide further generalization by allowing ( )i

dom A

being even a set of fuzzy sets (or possibility distributions). By enlarging ( )i

dom A , type-2 relations enable to

represent a wider type of impreciseness in data values. Such relations can be considered as a second levelgeneralization of classical relations.

Like classical relations, a fuzzy relation ris represented as a table with an additional column for ( )r

t ,

denoting the membership value of the tuple t in r. This table will contain only those tuples for

which ( ) 0r

t .

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3.1 Possibility Distribution

Instead of treating ( )F

u to be the grade of membership of u in F, one may interpret it as a measure of

the possibility that a variable X has a valueu , where X takes values in U. For example, the fuzzy setHigh-Salary may be considered as follows:

High-Salary = {0.5/20000, 0.6/30000, 0.7/40000, 0.9/50000, 0.1/70000}Suppose it is known that John has a High-Salary, then according to possibilistic interpretation, one concludesthat the possibility of John having salary=$ 40000/- is 0.7. Zadeh has suggested that a fuzzy proposition X is F, where Fis a fuzzy subset of Uand Xis a variable which takes value from U , induces a possibility

distributionX

which is equal to F(i.e.X

F ). The possibility assignment equation is interpreted as

( ) ( ), for allF

Poss X u u u U Thus the possibility distribution ofX is a fuzzy set which serves to define the possibility that X could

have any specified value u U . One may also define a function : [0,1]X

U which is equal toF

and

which associates with each u U the possibility that X could take u as its value, i.e.,( ) ( ) for

Xu Poss X u u U . The function

X is called the possibility distribution function ofX .

The possibility distributionX

may also be used to define a fuzzy measure on U where for anyf

A U ,

( ) ( ) sup ( )Xu A

A Poss X A u

4. Type-1 Fuzzy Relational Data Model

As discussed earlier, in type-1 fuzzy relations, ( )idom A may be a classical subset or a fuzzy subset of

iU . Let the membership function of ( )idom A be denoted by iA , for 1,2, ,i n . Then from the definition

of Cartesian product of fuzzy sets, 1 2( ) ( ) ( )ndom A dom A dom A is a fuzzy subset

of 1 2 nU U U U . Hence a type-1 fuzzy relation r is also a fuzzy subset of U with membership

functionr

. Also from definition of Cartesian product of fuzzy sets, for all *

1 2

( , , )n

u u u U ,r

must

satisfy, 1 21 2 1 2

( , , , ) min ( ), ( ), , ( )nr n A A A n

u u u u u u .

According to possibilistic interpretation of fuzzy sets,r

can be treated as a possibility distribution function

inU . Thus 1 2( , , , )r nu u u determines the possibility that a tuple t U has [ ]i it A u ,

for 1,2, ,i n . In other words, 1 2( , , , )r nu u u is a fuzzy measure of association among a set of domain

values 1 2{ , , , }nu u u .

Example 2. Consider a relation scheme LIKES(Student,Course), where dom(Student) and dom(Course) are

ordinary sets i.e., domain values are crisp. In the fuzzy relation rshown in the Table 2, ( )r t can be interpreted

as a possibility measure of a student liking a particular course. Thus the possibility of Soma liking OOP is 0.85.So,

r is a fuzzy measure of the association between Studentand Course.

Table 2. An instance rof LIKESStudent Course

r

Soma OOPS .85Roma DBMS .75John CG .8Mary DSA .9

It is also possible to provide an alternative interpretation ofr

as a fuzzy truth value belonging to [0,1] .

According to this interpretation, for a tuple , ( )rt t is the truth value of a fuzzy predicate associated with the

relation rwhen the variables in the predicate are replaced by[ ], 1,2, ,it A i n

. In many applications, it may

be necessary to combine both these interpretations of the membership function. For example, in entity

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relationship (E-R) Model (Date C.J.[4], Haier D.[6], Ullman J.D.[12]), one may interpretr

as the possibility

of association among the entities and follow truth value interpretation for membership of a tuple in the entitysets. In this connection, a paper by Zvieli and Chen [18] may be referred to, where fuzzy set theory has beenapplied to extend the E-R model and basic operations of fuzzy E-R algebra have been examined.

5. Type-2 Fuzzy Relational Data Model

Although type-1 relations enables to represent impreciseness in the association among data values, its rolein capturing uncertainty in data values is rather limited. For example, in a type-1 relational model for

Employee(Name, Salary), one is not permitted to specify salary of John to be in the range $ 40,000 - $ 50,000and that of Mary to be a fuzzy setLow. With a view to accommodate a wider class of data ambiguities, a further

generalization of the fuzzy relational data model has been considered where for any attribute , ( )i iA dom A may

be a set of fuzzy sets ini

U . As a consequence of this generalization, a tuple 1 2( , , , )nt a a a in

1 2( ) ( ) ( )nD dom A dom A dom A becomes a fuzzy subset of 1 2 nU U U U with

1 21 2 1 2

( , , , ) min ( ), ( ), , ( )nt n a a a n

u u u u u u

where , for 1, 2, ,i iu U i n . Since this equation holds for all , for 1, 2, ,i iu U i n , and

according to definition of fuzzy relation, a type-2 fuzzy relation ris a fuzzy subset ofD, where the membershipfunction : [0,1]r D must satisfy the following condition.

1 2

1 2

1 2

( , , , )

( ), ( ),( ) max min

, ( )nn

a a

ru u u U

a n

u ut

u

where 1 2( , , , )nt a a a D . As in the case of type-1 relations, r may be interpreted either as apossibility measure of association among the data values or as a truth value of fuzzy predicates associated with

r. Regarding the interpretation of a fuzzy data value ( )i ia dom A , the ia is treated as a possibility

distribution oniU . In other words, for a tuple 1 2( , , , )nt a a a D , the possibility of [ ]i it A u is

( )iA i

u .

Example 3. Suppose that an instance of the relation Employee(Name, Salary) may contains a tuple (John, S),where S={0:3/10000, 0:6/20000, 0:8/30000}. Here S represents the possibility distribution for the salary of Johni.e. Poss(Salary of John=30000)=0.8.Based on the possibilistic interpretation for the tuple tof fuzzy relation r, following is obtained,

1 1 2 2 1 2( [ ] , [ ] , , [ ] ) min{ ( ), ( , , , )}n n r t nPoss t A u t A u t A u t u u u

where , for 1, 2, ,i iu U i n . It is also possible to extend the above equation to find the possibility that

for a tuple 1 2( , , , ), [ ]n i it a a a t A a , where ia is a fuzzy subset of iU . Evaluation of such a condition is,however, related to the concept of compatibility of two fuzzy propositions [15, 10, 5].6. Data Types and Their Manipulation Methods

6.1 Data Types: FTS model considers all the eight different data types for fuzzy relational representationsproposed by Rundensteiner et al [11] as given below that correspond to the approach of Zemankova and Kandel[7, 17].(i) A single scalar (e.g. Aptitude=good),(ii) A single number (e.g. Age=22),(iii)Set of scalars(e.g. Aptitude={average, goodg},(iv) Set of numbers (e.g. {20, 21, 25}),(v) A possibilistic distribution of scalar domain values (e.g. Age={0.4/average, 0.7/good}),(vi) A possibilistic distribution of scalar domain values (e.g. Age={0.4/23, 1.0/24, 0.8/25}),(vii) A real number from [0,1] (e.g. Heavy=0.9),(viii) A designated null value (e.g. Age=unknown).

6.2 Arithmetic Operations

Fuzzy arithmetic is based on two properties of fuzzy numbers:1. Each fuzzy set, and thus also each fuzzy number, can fully and uniquely be represented by its -cuts;

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2. -cuts of each fuzzy number are closed intervals of real numbers for all [0,1] .These properties enable to define arithmetic operations on fuzzy numbers in terms of their -cuts. Thearithmetic operations on the set of intervals are defined as follows.Let { , , ,/} , then for all two intervals [ , ],[ , ]a b d e , following holds.

[ , ] [ , ] { | , }a b d e f g a f b d g e

except that [ , ],[ , ]a b d e is not defined when 0 [ , ]d e .The four operations on closed intervals are defined as follows:

[ , ] [ , ] [ , ], [ , ] [ , ] [ , ],

[ , ] [ , ] [min( , , , ),max( , , , )],

a b d e a d b e a b d e a d b e

a b d e ad ae bd be ad ae bd be

and provided that 0 [ , ]d e .

[ , ]/[ , ] [ , ] [1/ ,1/ ]a b d e a b e d min( / , / , / , / ),

max( / , / , / , / )

a d a e b d b e

a d a e b d b e

A real number v is regarded as a point interval[ , ]v v .Now two methods for developing fuzzy arithmetic are presented. One method is based on interval arithmeticwhich has recently been overviewed. The other method employs the extension principle, by which theoperations on real numbers are extended to operations on fuzzy numbers. Here it is assumed that fuzzy numbersare represented by continuous membership functions.Let A and B denote fuzzy numbers and let denote any of the four basic arithmetic operations. Then a fuzzyset A B is defined on R, by defining its -cut, ( )A B , as ( )A B A B for any (0,1] .

(When / , clearly, we have to require that 0 B for all (0,1] .)Due to first decomposition theorem of fuzzy sets (Klir George et al (1995)), A B can be expressed as

[0,1]

( )f

A B A B

Since ( )A B is closed interval for each (0,1] and ,A B are fuzzy numbers, A B is also a fuzzy

number. An example of employing above two equations, consider two triangular-shape fuzzy numbers A andB defined as follows:

0 for x -1 and x>3

( ) (x+1)/2 for -1

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0 for 6 and 2

( )( ) ( 6) / 4 for 6 2

(2 - ) / 4 for 2 2

x x

A B x x x

x x

1/ 2

1/ 2

1/ 2

0 for 5 and 15

[3 (4 ) ] / 2 for 5 0( )( )

(1 ) / 2 for 0 0

[4 (1 ) ] / 2 for 3 15

x x

x xA B x

x x

x x

0,for 1,and 3

( 1) /(2 2 ), for 1 0( / )( )

(5 1) /(2 2), for 0 1/ 3

(3 ) /(2 2), for 1/ 3 3

x x

x x xA B x

x x x

x x x

The second method of developing fuzzy arithmetic is based on the extension principle. Employing this principle,standard arithmetic operations on real numbers are extended to fuzzy numbers.

Let * denote any of the four basic arithmetic operations and let ,A B denote fuzzy numbers. Then a fuzzy set

A B on R is defined by equation ( )( ) sup min[ ( ), ( )]z x y

A B x A x B y

for all z R. More specifically it is defined for all z R as follows:

/

( )( ) sup min[ ( ), ( )]

( )( ) sup min[ ( ), ( )]

( )( ) sup min[ ( ), ( )]

( / )( ) sup min[ ( ), ( )]

z x y

z x y

z x y

z x y

A B x A x B y

A B x A x B y

A B x A x B y

A B x A x B y

Although A B is a fuzzy set on R, it is a continuous fuzzy number for each { , , ,/} [9].

6.3 Fuzzy Comparison operatorsFTS relational Model is designed to support the different data types as proposed by Rundensteiner et al (1989)for fuzzy relational representations that correspond to the approach of Zemankova and Kandel (1984, 1985). Tosupports queries that may contain qualifications involving imprecise and uncertain values, FTS relational modelis equipped with fuzzy comparison operators. These operators (EQ, NEQ) and (GT, GOE, LT, LOE) are definedas follows:

Definition 5: A resemblance relation, EQ ofUis a fuzzy binary relation on U U , that fulfills the followingproperties ,x y U , where U is the universe of discourse.

Reflexive : ( , ) 1EQ

x x , Symmetric : ( , ) ( , )EQ EQ

x y y x

Lemma 1: Let EQ be a resemblance relation on a setU. For all with 0 1 0, level setsEQ

are tolerance relation onU.

The concept of an -resemblance was introduced by Rundensteiner et al[11].

Definition 6: Given a set Uwith a resemblance relation EQ as previously defined. Then, ,U EQ is calleda resemblance space. An -level set EQ induced by EQ is termed as an -resemblance set. Define the

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relationship of two values ,x y U that resemble each other with a degree larger than or equal to (i.e. ( , )

EQx y ) as -resemblant. The following notation is proposed for the notion of two values

,x ybeing -resemblant: xEQ y . A set P U is called an -preclass on ,U EQ , if ,x y P , x and

y are -resemblant (i.e. xEQ y

holds).

To define fuzzy relations GREATER THAN ( GT) and LESS THAN (LT), let us consider a proximityrelation P defined as given below:

Definition 7: A proximity relation P over a universe of discourse U is reflexive, symmetric and transitive

fuzzy relation with 1 2( , ) [1,0]P u u , where 1 2,u u U (Kandel, 1986).

Definition 8: Let 1P is a proximity relation defined overU. Fuzzy relational operator GT is defined to be a

fuzzy subset ofU U , where GT satisfies the following properties 1 2,u u U :

1

1 2

1 21 2

0( , )

( , ) .GTP

if u uu u

u u otherwise

Definition 9: Let 2P is a proximity relation defined over a universe of discourseU. The fuzzy relational

operator LT is defined to be a fuzzy subset ofU U , whereLT

satisfies the following

properties 1 2,u u U :

1

1 21 2

1 2

0( , )( , ) .LT

P

if u uu uu u otherwise

Membership functions of fuzzy relations `NOT EQUAL' (NEQ ), `GREATER THAN OR EQUAL' ( GOE)

and `LESS THAN OR EQUAL' (LOE) can be defined based on that ofEQ , GT and LT as follows:

1 2 1 2

1 2 1 2 1 2

1 2 1 2 1 2

( , ) [1 ( , )]

( , ) max[ ( , ), ( , )]

( , ) min[ ( , ), ( , )]

NEQ EQ

GOE GT EQ

LOE LT EQ

u u u u

u u u u u u

u u u u u u

7. Fuzzy Relational Algebra & Tuple Calculus

Let ( , ( ))R Rt t t and ( , ( ))

S St t t be two fuzzy tuples in fuzzy relations R and S respectively

where ( ), ( )R S

t t denote their membership grades to R and S respectively. Fuzzy relational algebraic

operators are mounted with a flag f (viz , , , ,f f f f f f

) to distinguish them from their classical counterparts.Now, we introduce the formal definition of fuzzy relational operators as follows:

Table-3: Fuzzy RelationDeptDname Staff HoD Fund

r

Chem Null Jaya .63/low .63Eco Null Maya .63/mod .63Eco 10 Maya .60/mod .60

Chem 15 Jaya .63/mod .63

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Definition 10: Fuzzy ( )f

select s : ( ){ }, , ( ) , ( ) | ( . ) ( )pf

R R RR t t p t A t

a b as m m b=

where, ( . )p t Aa

is a fuzzy predicate that uses fuzzy comparison operators whose operands are fuzzy tuple

components ( . )t a . Value of a is used while deciding a -resemblance of two fuzzy values in the predicate. The

predicate is defined only on those tuples that qualify the threshold of tuple membership given by [ ]0,1b . Itgives another level of precision while applying fuzzy relational operations.

Table-4: Fuzzy Relation ,1,0.6 ( )HoD Mayaf

Dept

Definition 11: Fuzzy ( )

f

project p : ( ){ }, , ( ) . , min( ( . )) | ( )Af

A R RR t A t A t a bp m m b=

Table-5: Fuzzy Relation , ,0.8,0.6 ( )Dname Fundf

T Dept

Here equality of tuples has a special meaning. Two tuples from fuzzy relations are said to be equal iff each of itsattribute values (both crisp and fuzzy) are a -resemblant (i.e for the case of projected relation T, if

1 2,t t Dept and 1 2( . , . )EQ t A t Am a then 1t T if 1 2( . ) ( . )T Tt A t Am m otherwise 2t T ).

Definition 12: Fuzzy Cartesian Product(f

)

( ) ( )( ) ( ) ( ) [1] [1] [ ] [ ]( , ( ))

[ 1] [1] [ ] [ ] ( ) min( ( ), ( ))

r sfr s R S

T

T R S

u v t u t r u r T R S t t

t r v t r v u v t u v


Union

, ( , ( )) ( ( ) ( ) ) ( ( ) ( ) ) ( ) max( ( ), ( ))f

T R S T R ST R S t t R t t S t t t t t


Intersection

, ( , ( )) ( ( ) ( ) ) ( ( ) ( ) ) ( ) min( ( ), ( ))f

T R S T R ST R S t t R t t S t t t t t

Definition 15: Fuzzy Set ( )f

Difference :

, ( , ( )) ( ( ) ( ) ) ( ( ) ( ) ) ( ) ( )f

T R S T RT R S t t R t t S t t t t

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Subscript used with fuzzy operators is relevant for finding -resemblance of two fuzzy tuples.

Table-6: Fuzzy RelationR

Table-8: Fuzzy Relation .8,.5f

R S

Table-7: Fuzzy Relation S


R S


R S

8. Conclusion and Future ResearchIn this paper relational data model is extended in the sense that tuple components of fuzzy relations may

take crisp as well as fuzzy values. When tuple components are crisp but membership of tuple to the relation isfuzzy then it will generate fuzzy database of type-1. When tuple components itself are fuzzy then the fuzzydatabase generated is classified as type-2. An attempt has been done to define the five basic operations on fuzzydatabases that serve to redefine relational algebra & relational calculus. In type-2 fuzzy databases, a membershipvalue will always be associated with each data and hence a fuzzy relation has to sacrifice the First NF which is abottle-neck for its implementation and is yet to be resolved.

References[1] Baldwin, J F., (1983), A Fuzzy Relational Inference Language for Expert Systems. In Proc.13th IEEE

International Symposiam on Multivalued Logic. pp 416-423.[2] Buckles, B P. and Petry, E F., (1983), Information -Theoretical Characterization of Fuzzy Relational

Databases.IEEE Transactions on Systems, Man, and Cybernetics, Vol.SMC-13, No.1, pp 74-77.[3] Buckles, B P, Petry, F E and Sachar, H S., (1986), Retrieval and design concepts for similarity-based (fuzzy)

relational databases. ln Proc. ROBEXS'86, Houston, pp 243-251.[4] Date, C.J. (Ed.), (1981),An Introduction to Database Systems. AddisonWesley.[5] Dubois, D., & Prade, H. (Ed.), (1980), Fuzzy Sets and Systems: Theory and Applications. New York, PA:

Academic Press.

[6] Haier, D. (Ed.), (1983), Theory of Relational Databases. Rockville, Maryland, PA: Computer Science Press.[7] Kandel, A.,(1986), Fuzzy Mathematical Techniques with Applications", Addison Wesley Publishing Co.,California,.

[8] Kaufman, A., (1975), Inroduction to the Theory of Fuzzy Subsets", Vol-I, Academic Press, New York,Sanfrancisco,.

[9] Klir George J, & Yuan Bo (Eds.). (1995). Fuzzy Sets and Fuzzy Logic Theory and Applications. EnglewoodClis, N.J., USA, PA: Prentice Hall, Inc.

[10]Prade, H., Testemale, C., (1984), Generalizing Database Relational Algebra for the Treatment of Incompleteand Uncertain Information and Vague Queries.Information Science, pp 115-143.

[11]Rundensteiner, E A., Hawkes, L W and Bandler, W., (1989), On Nearness Measures in Fuzzy RelationalData Models.International Journal of Approximate Reasoning, (3), 267-298.

[12]Ullman, J.D. (Ed.), (1980), Principles of Database Systems. Rockville, Maryland, PA: Computer SciencePress,

[13]Umano, M, FREEDOM-O., (1982), A Fuzzy Database System. In Fuzzy Information and DecisionProcesses, M.M. Gupta and E. Sanchez Eds., North-Holland Pub. Co., Amsterdam, pp 339-347.

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[14] Umano, M., (1984), Retrival from Fuzzy Database by Fuzzy Relational Algebra. In Fuzzy Information,Knowledge Representation and Decision Analysis. E. Sanchez, Ed., IFAC Proc., Pergamon Press, Oxford,England, pp 1-6.

[15] Zadeh, L A., (1965), Fuzzy Sets, Information and Control, 8, pp 338-353.[16] Zadeh, L.A., (1981), PRUF - a meaning represenation langauge for natural language. In Mamdani, E.H., &

Gaines, B.R. (Eds.), Fuzzy Reasoning and its Applications, New York, PA: Academic Press, pp.1-66.

[17] Zemankova, M., Kandel, A. , (1984), Fuzzy Relational Database-A Key to Expert Systems", Verlag,TUV Rheinland, Cologne.[18] Zvieli, A., & Chen, PP., (1986), Entity-Relationship Modeling Fuzzy Databases. In Proc. Second Intl.

Conf. on Data Engg, pp. 320-327.

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Artificial Intelligence and Security

Awodele Oludele PhDComputer Science & Maths Department Babcock University, Nigeria.

[email protected]

AbstractSecurity is an important of the human lifestyle. Maintaining optimum security levels becomes absolutelynecessary in order to protect valuable information and assets. Artificial intelligence techniques have a veryimportant role to play in ensuring the effectiveness of security systems as applied in the world today. This paperexplains the need for digital security and the role of artificial intelligence in enhancing the effectiveness ofsecurity systems.

Keywords- Artificial Intelligence, Security, Digital Security& Data Mining

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1. Introduction

Security, which is the various means or devices designed to guard persons and property, has been an

integral need of humans through the ages, civilizations and empires that have come and gone. From thevery beginning, humans have tried to protect their privacy and enhance their security. Probably, the firstattempt of ancient humans to gain privacy and security was the use of caves [11]. As time passed, ancienthumans evolved and their intelligence and capabilities increased. The smarter humans became, the moresophisticated their privacy and security methods became.

These days, many security and privacy problems cannot be optimally solved due their complexity. Inthese situations, heuristic approaches should be used and artificial intelligence has proven to be extremelyuseful and well-fitted to solve these problems. Artificial neural networks, evolutionary computation,clustering, fuzzy sets, multi-agent systems, data mining and pattern recognition are a few examples ofartificial intelligence techniques that can be successfully used to solve some relevant privacy and security

problems.

2. The Need for Security

While there are different kinds of security, a consideration of the different types of security leads to thebroad classification of security into two categories, the field dealing with information technology, and thatdealing with physical security. However, in the technological age we live in, many systems have beencomputerized to the extent that ensuring physical security requires securing the computer system thatenforce the physical security. According to [9] many companies are spending billions of dollars to integrate

physical security with IT security.The need for security has grown even more pressing in recent times. The advancement of technology,

the Internet, and information sharing has had both positive and negative impacts. With the advancements intechnology also come problems. Threats to information systems that do not share information with anyother system are fairly minimal (Tarte, 2003). In most cases, security is accomplished by restricting

physical access to the computer system and then restricting users physically and electronically. Mostphysical security safeguards are adequate to protect this environment.

While physical security controls are still required for the protection of information systems that do notconnect with other systems electronically, the need for physical access to the system is now not required toaccess information. This problem now creates an open door for a multitude of possible threats toinformation and information systems that previously did not exist. Security of information therefore

becomes an absolute necessity.Internet security involves the protection of a computers internet account and files from intrusion by an

outside user. Organizations like the Center for Internet Security (CIS) is a not-for-profit organization thathelps enterprises reduce the risk of business and e-commerce disruptions result from inadequate technicalsecurity controls, and provides enterprises with resources for measuring information security status andmaking rational security investment decisions (CIS, 2003). The fact that an organization like CIS existstoday is proof of the importance of maintaining adequate internet security.

The advent of computing has simplified many things, but it also brings its own kind of crimes and theneed to implement security to prevent these crimes. The ease of use and anonymity provided by the

internet, which is to help users maintain privacy, has been misused by criminals. Terrorist organizationshave incorporated internet usage as part of the tools used in advancing such goals as fundraising andrecruitment [2] It has also been adopted for use as a means of communication and spreading propaganda asa result of the anonymity it provides [8] Terrorist-related sites proliferate on the internet to the extent thatsites which deal with such activities are referred to as the Dark Web [8] The problems with terrorism

being faced in contemporary times makes keeping track of such websites and gaining knowledge aboutthem paramount, as it will help in preventing, detecting and managing the threat terrorism makes on oursecurity [4]

The post 9/11 world has necessitated the rapid development of enhanced digital security for thetravelling public in order to satisfy the enhanced entry requirement for international border controlagencies. The airline industry has witnessed virtual demise of traditional paper tickets and the advent ofmore user-friendly electronic tickets. In addition, facial recognition technology is being considered in anattempt to reduce identity-related fraud. In fact, plans are underway in several countries, including the US,

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the UK, and Australia to introduce SmartGate kiosks with both retina and fingerprint recognitiontechnology (The Australian Department of Foreign Affairs and Trade, 2010).

The FBI, CIA and Pentagon are all leaders in utilizing secure controlled access technology for any of

their buildings. However, the use of this form of technology is beginning to pervade the entrepreneurialworld. A growing number of companies are beginning to take advantage of the development of digitallysecure controlled access technology.

Security affects all aspects of human lifestyle and has become increasingly important with the rapidadvancement in technology experienced in recent times. It therefore becomes paramount that effectivemethods be employed in ensuring that security is kept at optimum levels.

3. What is Artificial Intelligence?

Artificial intelligence (AI) is usually defined as the science of making computers do things that requireintelligence when done by humans. The goal of AI is to identify and solve tractable information processing

problems [5] Two of the most important and most used branches of AI are neural networks and expertsystems. An expert system can solve real-world problems using human knowledge and following human

reasoning skills. Knowledge and thinking processes of experts are collected and encoded into a knowledgebase. From that point on, the expert system could replace or assist the human experts in making complexdecisions by integrating all the knowledge it has in its knowledge base.

A very important application of neural networks is in pattern recognition. Humans, through neurons intheir brains learn how to read human writing, or identify their children from a set of kids. Neural networksallow computers to use the same principles that neurons in the brains use to recognize and classify different

patterns. Unlike humans, when a neuron is fully trained, it can classify and identify patterns in massiveamounts of complex data [14]

Artificial intelligence allows computers to learn from experience, recognize patterns in large amountsof complex data and make complex decisions based on human knowledge and reasoning skills. AI is a

broad subject and has become an important field of study with a wide range of applications in numerousfields.

3.1The Role of Artificial Intelligence in Digital SecurityDigital security and artificial intelligence in their early days did not seem to have much in common.However, the two fields have grown closer over the years, particularly where attacks have aimed tosimulate legitimate behaviours, not only at the level of human users but also at lower system layers.

When a system to be controlled is complex and has to operate under various conditions anddisturbances, artificial intelligence techniques offer an excellent alternative. Incorporating artificialintelligence into such systems allows these systems to be more flexible, to adapt to various operatingconditions and disturbances, and to incorporate human expertise and thinking into their decision process.Presently, the rising complexity of security problems make them more difficult to solve, which is whyartificial intelligence has proven to be an optimal solution to such problems [11]

Artificial Intelligence techniques such as neural networks (pattern recognition), biometrics, data-mining, and intelligent agent systems are some techniques that are of great importance in solving thesesecurity problems.

3.2AI Applications in Security

3.2.1 Data MiningData mining, the discovery of new and interesting patters in large datasets, is an exploding field. Data

mining is often considered as a blend of statistics, artificial intelligence and database research [7].Recently, there has been a realization that data mining has an impact on security. One aspect is the use ofdata mining to improve security in such instances as intrusion detection. Data mining is often used as ameans for detecting fraud and assessing risk. Data mining involves the use of data analysis tools to discover

previously unknown, valid patterns and relationships in large data sets. These tools can include statisticalmodels, mathematical algorithms, and machine learning methods (such as neural networks or decisiontrees). Data mining is becoming increasingly common in both the private and public sectors. In the publicsector, data mining applications initially were used as means of identifying fraud [12] even identifies data

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mining as a potential means to identify terrorist activities, such as money transfers and communications,and to identify and track individual terrorists themselves such as through travel and immigration records.Data mining can play a major role in security systems especially in areas such as fraud detection and

intrusion detection. In turn, artificial intelligence techniques help improve the quality of data mining.

3.2.1. Network SecurityIntrusion detection is designed to detect attempts by intruders to violate the security mechanisms of acomputer system or network as early as possible. To achieve their goals, intruders exploit variousweaknesses inherent in complex systems. Intrusion detection systems face enormous challenges, thecomplexity of which should not be underestimated. Artificial intelligence (machine learning) techniques areexcellent tools for improving network security as they provide new approaches to intrusion detection.Knowledge bases (expert systems) form a very vital part of intrusion detection systems. Artificial neuralnetworks also provide the potential to identify and classify network activity based on limited, incompleteand nonlinear data sources. [13] have even proposed a network risk assessment and network monitoringapplication that relies on knowledge-based artificial intelligence technologies to improve on traditionalnetwork vulnerability assessment. In general, AI techniques in combination with other methods typically

result in an improvement on traditional network security measures.

3.2.2 Pattern RecognitionPattern recognition is a branch of artificial intelligence that studies the operation and design of systems thatrecognize patterns in data. It encompasses areas such as discriminant analysis, feature extraction, errorestimation, cluster analysis, grammatical inference and parsing (Pattern Recognition Group at DelftUniversity of Technology, 2010). Pattern recognition plays an integral part in biometrics. The field of

biometrics examines the unique physical or behavioural traits that can be used to determine a personsidentity. Biometric recognition is the automatic recognition of a person based on one or more of these traits.The word biometrics is also used to denote biometric recognition methods [6] Biometric traits, forexample, include fingerprint, face or even iris. Biometric technology is typically used to prevent fraud,enhance security and curtail identity theft. Pattern recognition plays a major role in many of theauthentication and authorization mechanisms employed in the world today with biometrics being theleading example. Pattern recognition is also integral to the workability of digital signatures.

3.2.3 Steganography and WatermarkingInformation security plays a major role in the case of secured data transmission. Data security, availability,

privacy and integrity are very important issues in the world today. Steganography is the study of techniquesfor hiding the existence of a secondary message in the presence of a primary message. Steganography itselfoffers mechanisms for providing confidentiality and deniability [15] Steganography and watermarkingdescribe methods to embed information transparently into a carrier signal. Watermarking generally has theadditional requirement of robustness against manipulations intended to remove the embedded informationfrom the marked carrier object. [15] proposed an artificial intelligence approach to audio steganography.The proposed approach made use of a genetic algorithm. The use of the AI approach led to increasedrobustness against intentional attempts to reveal the hidden message and also against some unintentionalattacks such as noise addition.

4. Conclusion and Future ResearchSecurity is an important part of our daily lives and technology has a very vital part to play in ensuringadequate security. With the rising complexity of security issues, artificial intelligence techniques go a longway in improving the effectiveness of security systems thereby ensuring the protection of information. Theuse of various artificial intelligence techniques such as neural networks (pattern recognition), expertsystems and data mining ensures an increase in the reliability of security systems. Artificial intelligencetherefore has an important role to play in implementing security systems that would be capable of standingup to the security challenges we face in our world today.

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References[1] Australian Department of Foreign Affairs and Trade (2010). Retrieved April, 2010 fromhttp://www.dfat.gov.au/dept/passports/

[2] Bowers, F. (2004). Terrorists spread their messages online. Christian Science Monitor, July 28, 2004

[3] Center for Information Security (2003). http://www.cissecurity.org

[4] Damianos, L. et al. (2002)MiTAP for Biosecurity: A Case Study, AI Magazine, 23(4), 1329.

[5] Marr, D (1976). Artificial Intelligence A Personal View. Massachusetts Institute of TechnologyArtificial Intelligence Laboratory. Retrieved April 2010 fromhttp://courses.csail.mit.edu/6.803/pdf/marr.pdf

[6] MSU Biometrics Group (2010). What is Biometrics? Retrieved April 2010 fromhttp://biometrics.cse.msu.edu/info/index.html Pattern Recognition Group at Delft University(http://www.ph.tn.tudelft.nl/)

[7] Pregibon, D. (1997).Data Mining. Statistical Computing and Graphics, 7, 8.

[8] Qin, J., Zhou, Y., Reid, E., Lai, G., and Chen, H. (2007)Analyzing Terror Campaigns on the Internet:Technical Sophistication, Content Richness, and Web Interactivity. International Journal of Human-Computer Studies 65, 71-84.

[9] Scalet, S.D. (April, 2005) Case Study: Security Convergence. Retrieved April, 2010 fromhttp://www.csoonline.com/article/220278/Case_Study_Security_Convergence

[10] Security and protection system. (2010). InEncyclopaedia Britannica . Retrieved April, 2010 from

Encyclopaedia Britannica Online: http://www.britannica.com/EBchecked/topic/532067/security-and-protection-system

[11] Solanas, A. and Martnez-Ballest, A. (2009)Advances in Artificial Intelligence for Privacy ProtectionandSecurity.Seifert

[12] W. J. (2007). Data Mining and Homeland Security: An Overview. CRS Report for Congress.Retrieved April 2010 from http://www.fas.org/sgp/crs/intel/RL31798.pdf

[13] Shepard, B., Matuszek C., Fraser, C. B., Wechtenhiser W., Crabbe, D., Gungordu Z., Jantos, J.,Hughes T., Lefkowitz, L., Witbrock, M., Lenat, D., Larson, E. (2005).A Knowledge-Based Approachto Network Security: Applying Cyc in the Domain of Network Risk Assessment. American Associationfor Artificial I

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