a novel secure energy efficient spectrum ...a novel secure energy efficient spectrum sensing...

A NOVEL SECURE ENERGY EFFICIENT SPECTRUM SENSING TRANSACTION

MODEL TO IMPROVE THE PERFORMANCE CRN USING SEMI MACHINE

LEARNING TECHNIQUES

N. Priya1, Dr.B. Rosiline Jeetha

2

ABSTRACT:

The area of security in Cognitive Radio has yet to receive much attention to improve

the accuracy level even though there is no correct accuracy to formulate the problem .The

Paper proposed Secure Energy Awareness Spectrum Sensing Transaction (SEASST) method

are based on semi machine learning technique. This method is used to improve the energy

level considering the parameters like Spectrum Sensing, Channel Allocation and Data

Transaction through increase of the energy efficiency. The result is proved by Network

Simulator version2.35.

Key words: Sensing, Security, Energy Awareness, Semi Machine Learning.

INTRODUCTION

In the area of communication industry, wireless communication is the fastest growing

one and it has attracted the attention of media and the imagination of the public. Cellular

system has experienced a fast growth over the last decade and currently. There are around

two million users worldwide cellular phone have become an integral part and tool of

everyday life in most developing countries and it is substituting the old wireless systems in

many developing countries. But the main problem with wireless communication is spectrum

scarcity. So FCC introduced CR overcome this. The main drawback of SDR is that, it has no

intelligence and cannot take decision of its own. It consumes high power, has higher initial

cost and also requires higher processing power. A cognitive radio is an intelligent radio that

can be programmed and configured dynamically through cognitive capability and Re-

configurability. In cognitive capability, the CR learns and observes the surrounding and

comes with a decision and based on this decision the CR to reconfigures the hardware and

software. To reconfigure the SDR technology is used which all the function were performed

software instead of using hardware when this SDR joins with CR, the spectrum utilization

and communication efficiency are improved. The strength of CR is six stage cognitive cycle

includes observation, orientation, plan, Decision making, Action and Learning. Four

functions that mainly used in CR are Spectrum sensing, Spectrum management, spectrum

sharing and spectrum Mobility. In this spectrum sensing the main problem is to improve in

International Journal of Pure and Applied MathematicsVolume 119 No. 18 2018, 1625-1637ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

1625

security level, channel allocation and transaction management. There are Selfish and

Malicious Attackers, Power-fixed and Power Adaptive Attackers and Static & Mobile

Attackers are founded in cognitive radio. In each layer there are different attacks if that

attack have been cleared in one layer it will attack the other layer for that cross layer. In that

mainly focused on PUE attack and Spectrum Sensing Data Falsification are the major

attacks that cause high lacking in spectrum sensing. If this two attack has been overcome

and the channel is selected and transaction is done it will improve the spectrum sensing

accuracy level and trough put level .In previous research channel selection was done by

various mechanism by machine learning supervised and unsupervised but in this paper semi

supervised machine learning is used so it will show a better result. For the better results

certain process have been under gone here, In this paper, channel allocation problem to

improve the Spectrum Sensing takes place to overcome that secure based cross layer

approach method (SCAM) is used to detect miss probability after this channel selection is

done in supervised manner. In upcoming process it should done through semi-supervised in

secure feature extraction based kernel canonical correlation analysis (SFEKCCA) technique

and channel should allocated and test the stage to get the new optimized channel and transfer

the data in between the channels while transfer the energy should be calculated by correct

detection and miss detection and get the total energy consumption by Energy Efficient

Secure Transaction Scheme (SEESST) base on this average is calculated.

Related work:-

Spectrum Sensing Data Falsification attack (SSDF) is present in cooperative spectrum

sensing. In this attack is found when multiple users used it. Various researches have been

analyzed and overcome to SSDF attack.[1] The author has analyzed pervious research

method in weighted sequential probability ratio test (WSPRT) and enhanced to recover the

technique by using Reputation value (RV).The proposed technique Robust weighted

sequential probability ratio test (RWSPRT), it deal with various attack and find the correct

probability. This technique is proved in simulation results. Primary User Emulation Attack

(PUEA) occurs due to the Secondary User to access unoccupied spectrum band at the

primary User signal cause the attack. To overcome this problem the authors[2] investigated

various methods future the author describe a new method channel impulse response

(CIR).The proposed detection method is used to detect whether the received signal of the PU

or PUEA. To find out the Process are based on between Secondary Users and Signal source.

The performance of proposed method was analyzed and proved by simulation result.

International Journal of Pure and Applied Mathematics Special Issue

1626

METHODOLOGY

First consider channel allocation problem to improve the Spectrum Sensing takes

place in that energy level, signal noise ratio, locality size will be compared and to get the

result by using SCAM method. To find the miss detection false alarm probability and

detection should be find once and it will be the total correct probability and that checked in

global to know whether it idle or busy and that result consider as total miss detection

probability. After that work channel selection is supervised but it does in limited area with

security to overcome that semi-supervised technique takes place and node will be analyzed

and ready for the channel selection process and sense signal by using hierarchical cluster

based- FEKCCA technique and test the stage and send the result to FC to check the feature

extraction along with the security level to find the new space and allocate the new optimized

channel. The previous work outcome, once the channel is optimized, it is ready for

transaction. During the transaction period data security is most need and it is not much

efficient to overcome. Therefore, to design the Secure Energy Awareness Spectrum Sensing

Transaction scheme (SEESST) well addressed for both problem-in this method using

transaction progression is being transferred from the source side channel is using the

cryptography signature and to the destination side channel is using bond signatures, both are

verified, and also simultaneously aware of the energy level, they are used correction

detection, miss detection by this total energy consumption.

Problem Formulation:

Spectrum sensing:

Secondary User to access unused spectrum band when they causes the Secondary

User Date Falsification Attack and Primary User Emulation Attack these two attack are

mostly dangers for accurate Spectrum sensing. It can be overcome by SCAM method. SCAM

method procedure can be classified into SDFA and PUEA. SDFA to identify by used energy

level and signal noise ratio and the second to identify the PUEA by used locality value

through fingerprint and energy level through threshold value. The process of detection can be

represented as a binary hypothesis testing problem.

H0: rs(t) = an(t)

H1: rs(t) = cg* ts(t) + an(t) -> (1)


1627

The first step is to take the sample hypothesis test and add Gaussian noise. The

following is an example in the case of primary user of the channel is shown as H0, thus the

primary user is absent. If it is given as H1 then the primary user is present .In the case of

secondary user, the received signal is denoted as rs(t) and time as t. ts(t) is the transmit

signal.cg is the channel and an(t) is the Gaussian noise. Ed = |rs t |^22𝐿𝑡=1 .In Ed L is

maximum enough signal noise ratio should find by Gaussian random variable.H0: GRV ~ N

(µ0, σ20)

H1: GRV ~ N (µ1, σ21) -> (2)

Where, µ0=2L, σ2

0 =4L, µ1= 2L (ⱷ+1), σ21 =4L (2ⱷ+1), ⱷis the received SNR of the SU. If

SNR is finalized it should compare with local threshold. ED φ. If it is maximum

spectrum sensing performance should be analyses by using Probability of false alarm

probability of detection are used. The probability of false alarm pfa= P=P(x=1|H0) = Z (ⱷ-

µ0/σ0), the probability of detection pcd=P(x=1|H1) = Z (ⱷ-µ1/σ1), Where Z(X) is the

Gaussian Z-function. The probability of miss-detection 𝑃𝑚d=1−𝑃𝑑= (𝑌<ⱷ|𝐻1).Based on the

upcoming results Total correct probability is calculated.

𝑃=p (𝐻1) + (1−pfa)p(𝐻0) ->(3)

Based on the miss-detection, the malicious node can be found out subsequently the

performance of malicious sensor is formulate, PMF = Pfa * (1- Prob) + (1 -PMF)* Prob,

PMD= Pod * (1- Prob) + (1 -PMD)* Prob.

The SU transfer the data to local decision and Global decision will evaluate the node

whether it’s idle or busy once it idle, channel will try to access the node. If the node is

success it will go further or it create a collision and it will get reevaluated SU and send the

alarm by REF = P(R=1|H0),=P(R=1|H0, F=0)*P(F=0|H0)+P(R=1|F=1,H0)*P(F=1|H0)

= 0+P^QF -> (4)

If Global decision whether the result is busy it will wait for the compulsion of the current

frame work. Then next GD, It will get the final total Miss Probability can be obtained by

using below equation QMP= p(R=0|H1), =P(R=0|H1, F=0)*P(F=0|H1)+P(R=0|F=1,H1)*

P(F=1|H1)

= 0+PXm*(1-QG) -> (5)


1628

Secure Cross layer approach method to detect miss detect probability (SCAM)

1. Secure Cross layer approach method

2. rs(t)=0

3. rs(t) is broadcasted

4. if(H=0 || H=1)

5. then

6. energy detector is calculated

7. if(L>10)

8. then

9. if(H=0 || L>10)

10. then

11. signal noise ratio of H0 is calculated

12. if(H=1 || L>10)

13. then

14. signal noise ratio of H1 is calculated

15. T=0.5

16. if(H>T)

17. then

18. Pfa and Pcd is detected

19. Pmd is calculated

20. Tcp is calculated

21. Tcp and Behaviour of the malicious nodes are detected then sent to the Ld

22. End if

23. End if

24. End if

25. End if

26. Gd(F) evaluates the local decision data

27. if(F=0)

28. then

29. rs(t)->PU channel

30. if(rs(t)=0)

31. then

32. channel gain

33. End if

34. else

35. Reevaluation process takes place

36. Decision is declared

37. End if

38. If(F=1)

39. then

40. Secondary user waits

41. End if

42. Finally Find the total miss probability is obtained

43. End if


1629

Channel Allocation

Based on the above result, supervised is used to channel selection but its selected in

limited area with security to overcome that in following work semi-supervised technique is

takes place with same security level and node will be analyzed and ready for the channel

selection process and sense signal and move to Local-to- FC. FC takes place and applies

FEKCCA technique for the feature that provides maximum correction among the SU.FC

analyze and initial test the stage and checked in autonomous testing or cooperative testing

and get the result in both way and send again to FC and test the energy and cyclic feature and

find the space. Kernel-based Learning is data transformed into a elevated dimensioned

feature space find by using,

Ψ ∶ 𝑦𝑖𝑛 → Ψ(𝑦𝑖𝑛 ) -> (6)

Explicit calculating the new space may be hard to find dimensionality and it’s possible to

calculate inner product and security level by using mercer’s condition formulated by

Ker(yij,yix)=𝜓((yij),𝜓 (yix)).The space induced by a kernel that considers the polynomial

kernel by two dimensional feature vectors that composed of energy level. This kernel can be

expanded in individual by using (𝑦𝑖𝑇𝑦𝑗 )2 = (𝑓𝑖1𝑓𝑗1 + 𝑓𝑗2𝑓𝑗2)2

.Feature mapping takes the

form, which correspond to three dimensional feature spaces, this polynomial kernel used in

more general formulate as 𝐾𝑒𝑟 𝑦𝑖𝑗 , 𝑦𝑖𝑘 = (𝑦𝑖𝑗𝑇𝑦𝑖𝑘 + 𝑐) q. It considers the stand Gaussian

kernel is given by𝐾𝑒𝑟 𝑦𝑖𝑗 ,𝑦𝑖𝑘 =exp (− 𝑦𝑖𝑗−𝑦𝑖𝑘 2/2𝑤𝑖2) .It contains infinitely-dimensional.

𝐾 𝑥𝑖𝑗 , 𝑥𝑖𝑘 = exp( 𝑥𝑖𝑗 − 𝑥𝑖𝑘 2/2𝑤𝑖

2) -> (7)

Feature space by sub index. The gram matrix Ki for the data set is used to find pair-wise

kernel of the data as its element.

𝐺𝑖 𝑗,𝑘 = 𝐾𝑒𝑟 𝑦𝑖𝑗 , 𝑦𝑖𝑘 = Φ(𝑦𝑖𝑗 )TΦ 𝑦𝑖𝑘 ->(8)

Kernel canonical correlation Analysis for CSS that consider a scenario in which M SU

present and get the order of correlation between multiple data sets and each pair is used in

canonical correlation between the data set 𝛽i can be subsequently obtained as

𝛽𝑖 . = 1

𝑁−1 𝛽𝑖𝑗 .

𝑁𝑗=1 ->(9)

Trivial solution is used to avoid the canonical variants 1

𝑁 𝓍𝑖

𝑁𝑖=1

2=1

𝑁 𝛼𝑖

𝑇𝐺𝑖𝐺𝑖𝛼𝑖𝑁𝑖=1 = 1.In

next step over-fitting problem can be avoided and eliminate the norm of projectors


1630

by1

𝑁 𝛼𝑖

𝑇𝐺𝑖𝐺𝑖𝛼𝑖𝑁𝑖=1 + 𝑐𝛼𝑖

𝑇𝐺𝑖𝛼𝑖 = 1. The canonical weight Xi is used to overcome the

problem with Lagrange Multipliers by generalize Eigen value ,1

𝑁𝐿𝛼 = 𝛾𝐸𝛼.The selection

that contains canonical weight to find GEV problem and Cooperative list the FC and get the

result. The CCA requires the data and KCCA applies the CCA feature to find the new

space, 𝜓(𝑦𝑖𝑛𝑁𝑖=1 ) = 0, 𝑖 = 1,… ,𝑁, To find the Gram matrix

𝐺𝑖~ = 𝑁0𝐺𝑖𝑁0𝑇 ->(10)

After KCCA it obtains the non-liner local detection. Kernel function calculated the center

data by canonical vector and statistical tested. If it enter the FC by expansion of data through

sensing device in order to compute and if global test is statistic is simply obtained𝑇𝑖 𝑦𝑖 =

𝛼𝑖𝑗𝑘𝑒𝑟 (𝑦𝑖 ,𝑦𝑖𝑗 )𝑁𝑖=1 ,𝑇𝑖 𝑦𝑖 = 𝑇𝑖(𝑥𝑖)

𝑁𝑖=1 , Finally result is an improved detection performance

is satisfied.

Hierarchical cluster based SFEKCCA method for CSS Channel allocation

Step 1:- Assuming the sample takes as secondary user from that select the channel of

primary user

Step 2:- The information is send to the fusion center.

Step 3:- Based on the feature of FC, SFEKCCA technique is implemented.

Step 4:- Through elevated-dimensional a new space is to be found and calculate inner

products which are being used by polynomial kernel.

Step 5:- In Feature extraction the measurement is report (autonomously and cooperatively)

to fusion center while each time channel is selected.

Step 6:- Fusion center extract the statistical test and distributed the features to each SU with

high security level.

Step 7:-Fusion-center used CCA method used to test local and global and finds the new

space for channel allocation.

Data Transaction

Previous result based on, channel is selected and it ready to transactions the secure

data from one channel to another channel but while transfer data security ,energy level is not

that much efficient so to overcome that certain secure energy aware spectrum sensing


1631

transaction methods are used in the phase. While transfer the data energy level should

effectively use .so the transformation the data security, speed, time and energy level should

be calculated. While transfer the data certain methods should be takes place, and verified the

security signature.

Secure Energy Awareness Spectrum Sensing Transaction scheme (SESST)

Step 1:- The channel is ready to transact the secure data

Step 2:- Energy level is efficiently calculated by Energy Efficient Secure Transaction

methods

Step 3:- For Energy Efficient CR security and weighted method combined and gets the miss

detection.

Step 4:- during transaction progression is being verified the security signature like that

contain cryptography signature and bond signatures, both are verified.

The CR security transmission channel capacity of correct detection is calculated using

the formula 𝑠𝑐𝑑𝑐 = 𝐵𝑙𝑜𝑔2 1 +𝑃𝑐𝑠𝑚𝑕

𝑁0𝐵 . After calculating it weighted transmission capacity of

CD is found using another formula𝐶𝑤𝑑 (𝑤𝑒𝑖𝑔 𝑕𝑡𝑒𝑑 ) = 𝐵𝑙𝑜𝑔2 1 +𝑃𝑐𝑠𝑚𝑕

𝑁0𝐵 𝑃 𝐻0

𝐻0 𝑃(𝐻0). Then

the transmission capacity of misdetection is calculated with 𝐶𝑚𝑑 = 𝐵𝑙𝑜𝑔2 1 +𝑃𝑐𝑠𝑚𝑕

𝑁0𝐵+𝑃𝑝𝑕 and

also the weighted transmission capacity of misdetection is measured by making use of the

formula𝐶𝑡𝑚𝑑 (𝑤𝑒𝑖𝑔 𝑕𝑡𝑒𝑑 ) = 𝐵𝑙𝑜𝑔2 1 +𝑃𝑐𝑠𝑚𝑕

𝑁0𝐵+𝑃𝑝𝑕 𝑃 𝐻0

𝐻1 𝑃(𝐻1). Based on the miss detection

average capacity and total energy consumption is fined. Average channel capacity

𝐶𝑎𝑣𝑒 = 𝐵 𝑙𝑜𝑔2 1 +𝑃𝑐𝑠𝑚𝑕

𝑁0𝐵 1 − 𝑍𝑓 𝑃 𝐻0 + 𝑙𝑜𝑔2 1 +

𝑃𝑐𝑠𝑚𝑕

𝑁0𝐵+𝑃𝑝𝑕 𝑍𝑚𝑃(𝐻1) -> (11)

Total energy consumption

𝐸T = 𝑃𝑇𝑇𝑇 + (𝑃𝑆𝑇𝑆 + 𝐸𝑑t+ EC) ->(12)

Average energy efficiency calculated by the bit per watt

𝐴EF =𝑇𝑇𝐶𝑎𝑣𝑒/ 𝐸𝑡𝑜𝑡𝑎l ->(13)

So here energy level much better.


1632

Simulation Result& Discussion:

The simulation Result analysis for Secure Energy Aware Spectrum Sensing

Transaction Method (SEASST) is done by using Network Simulator 2.35. The proposed

method has Improved energy level through SEASST method by considering three parameter

like Spectrum sensing, Channel Allocation, Data transaction. In this operation Network Size

has been chosen as 1000*1000 m with 100 nodes along with wireless channel types. AODV

protocol is used to develop, proposed methods like SCAM, SFEKCCA while comparing the

existing methods like Blind-Channel-Estimation and RWSPRT. Two ray ground models are

needed for radio propagation. Constant Bit Rate is used from the source to destination

transaction service. Omni Antenna value has the Antenna model which is used for including

the interface queue type Drop Tail/Priqueue. After that the total channel used is 5 with the

simulation of time is as 200per second. The range of frequency used is 250m and 1mbps

traffic rate are used CBR traffic model. Random model is used to check mobility. MAC

protocol is 802.11 and flat grid needed by Topologic flat grid is used.

No. of Attackers

False alarm Probability for SU


1633

False alarm probability for PU

Detection Probability for SU

Dropping Rate for PU

CONCLUSION:

In this paper, SCAM, SFEKCCA methods have been proposed SEASST to

overcome the cognitive radio network security issues. This proposed semi machine learning


1634

technique is preventing CRN, to provide high efficient energy level. This simulation results

showed the performance of our proposed approach and provide the high energy awareness

by the following performance metric are used such as Secondary user (find the No. of

Attacker, false alarm, detection probability) and the Primary User is used to (find false alarm

probability and dropping rate) to get the result. The proposed method shows that the detection

probability has been improved and the dropping rate has been reduced. The SEASST method

is used for further analysis in future in the real time scenario.

References:-

[1].Jun Wu, Tiecheng Song, Cong Wang, Yue Yu, Miao Liu, Jing Hu "Robust Cooperative

Spectrum Sensing Against Probabilistic SSDF Attack in Cognitive Radio Networks”."

ISBN: 978-1-5090-5935-5 IEEEXplore, pp. 1-6, 2017.

[2].Qiao-mu JIANG1, Hui-fang CHEN‡1,2, Lei XIE1,2, Kuang WANG1,2 “On detecting

primary user emulation attack using channel impulse response in the cognitive radio

network", ”Springer link ,ISSN 2095-9230 (online),pp.1665-1676,2017.

[3].M. R. Manesh, A. Quadri, S. Subramanian, and N. Kaabouch, "An Optimized SNR

Estimation Technique Using Particle Swarm Optimization Algorithm,” IEEE Consumer

Communications and Networking Conference, pp. 1-7, 2017.

[4].W. L. Chin, T. N. Le, C. L. Tseng, W. C. Kao, C. S. Tsai and C.W. Kao, “Cooperative

detection of primary user emulation attacks based on channel-tap power in mobile cognitive

radio networks,” in Int. J. Ad Hoc and Ubiquitous Computing, Vol. 15, No. 4, pp. 263-274,

May 2014.

[5].J. Wang and I.R. Chen, “Trust-based Data Fusion Mechanism Design in Cognitive Radio

Networks," IEEE CNS Workshop on Cognitive Radio and Electromagnetic Spectrum

Security, 2014.

[6].Kun Zeng, QiHang Peng, YouXi Tang, "Mitigating spectrum sensing data falsification

attacks in hard-decision combining cooperative spectrum sensing", Science China

Information Sciences April 2014, Volume 57, Issue 4, pp 1-9.

[7].Saduyu, Y.E. Securing Cognitive Radio Networks with Dynamic Trust against Spectrum

Sensing Data Falsification, Proceedings of IEEE Military Communications Conference,

(2014), October 6-8; Baltimore,MD.


1635

[8]. Chatterjee, K., De, A., & Gupta, D.: A secure and efficient authentication protocol in

wireless sensor network. Wireless Personal Communication, vol. 81(1), pp. 17-37 (2015)

[9] S. Vassaki, M. I. Poulakis, and A. D. Panagopoulos, “Spectrum leasing in cognitive radio

networks: A matching theory approach,” in 81th IEEE VTC, Glasgow, May 2015.

[10]. J. Wang, I.-R. C, J. J. P. Tsai and D.-C. Wang, “Trust-based cooperative spectrum

sensing against SSDF attacks in distributed cognitive radio networks,” IEEE International

Workshop Technical Committee on Communications Quality and Reliability, 2016.

BIOGRAPHY

Ms .Priya. N received B.Sc degree in Information Technology under

Mother Teresa Women’s University, Kodaikanal. M.Sc (CS) and

M.Phil under Bharathir University respectively, Currently Pursuing

Ph.D (Full Time) form Dr N.G.P Arts and Science College ,which is

affiliated to Bharathiar University, Coimbatore. Her research interest

includes the areas of cognitive radio, wireless security, and machine

learning.

.Dr. B. Rosiline Jeetha graduated B. Sc. (Comp. Sci.), MCA and M.

Phil., and Ph. D. from Bharathiar University, Coimbatore. At present

she is working as Head and Professor in the PG and Research

Department of Computer Science at Dr. N.G.P. Arts and Science

College, Coimbatore. Her research interest lies in the area of Data

Mining and Networks. She has 15 years of collegiate teaching and 13

years of research experience

.


1636

a novel secure energy efficient spectrum ...a novel secure energy efficient spectrum sensing...

Documents