SRS OF

MULTI-LEVEL INTRUSION DETECTION SYSTEM AND

LOG MANAGEMENT IN CLOUD COMPUTING

BY

JAMES. KALLEPALLI

MCA FINAL YEAR

AMRITA SAI INSTITUTE OF SCIENCE AND TECHNOLOGY

PARITAL

MULTI-LEVEL INTRUSION DETECTION SYSTEM AND

LOG MANAGEMENT IN CLOUD COMPUTING

Abstract

Cloud computing is a new type of service which provides large scale

computing systems can be easily threatened by various cyber attacks, because

most of cloud computing systems provide services to so many people who are

not proven to be trustworthy. So a cloud computing system needs to contain

some intrusion detection systems (IDSs) for protecting each virtual

machine(VM) against threats. In this case, there exists a tradeoff between the

security level of the IDS and the system performance. If the IDS provide

stronger security service using more rules or patterns, then it needs much more

computing resources allocating for customers decreases. Another problem in

cloud computing is that, huge amount of logs makes system administrators

hard to analyse them.

The intrusion detection is defined as a mechanism for a WSN to detect

the existence of inappropriate, incorrect, or anomalous moving attackers. For

this purpose, it is a fundamental issue to characterize the WSN parameters such

as node density and sensing range in terms of a desirable detection probability.

In this, I consider this issue according to two WSN models: homogeneous and

heterogeneous WSN. Furthermore, I derive the detection probability by

considering two sensing models: single-sensing detection and multiple-

sensing detection. In addition, I discuss the network connectivity and

broadcast reachability, which are necessary conditions to ensure the

corresponding detection probability in a WSN. Our simulation results validate

the analytical values for both homogeneous and heterogeneous WSNs.

Another important problem is log management. Cloud Computing

systems are used by many people, therefore, they generate huge amount of

logs. So, system administrators should decide to which log should be analysed

first.

In this I propose Multi-Level IDS and log management method based on

consumer behaviour for applying IDS effectively to Cloud Computing system.

Cloud Computing technology provides human to advantages such as

economical cost reduction and effective resource management. However, if

security accidents occur, ruinous economic damages are inevitable. I proposed

Multi-level IDS for effective resource and log management. Proposed method

provides how we decrease the rule-size of IDS and manages user’s logs.

Existing System:

There has been a recent awareness of the risk associated with network

attacks by criminals or terrorists, as information systems are now more open to

the Internet than ever before. Records made available by the Pentagon showed

that they logged over 79,000 attempted intrusions in 2005 with about 1,300

successful ones

we are detect with small about of extension appiled to detect.

At all detection the technology beyond with detection getting delay in

Network.

It will be not used to the environment to detect the network.

Network will be always busy in this scenario.

Proposed System

I propose the method for maintaining strength of security while minimizing

waste of resources and analyzing logs efficiently.Our method increases

resource availability of cloud computing system and handle the potential

threats by deploying Multi-level IDS and managing user logs per group

according to anomaly level. We can suppose that VMs have equal quantity of

resource, then host OS can assign less guest OS with IDS, because IDS use

much resources. Our method supports classifying the logs by anomaly level,

so it makes system administrator to analyse logs of the most suspected users

first. By this our methods provides high speed of detecting attacks.

There is no room for delay in the network.

The possibility to detect the network in the environment is more.

provides better performance in terms of accuracy and cost.

Proposal multi-level IDS Architecture.

Block Diagram

Intrusion detection in a WSN.

MODULES

Network Model

Classification Model

Intrusion Strategy Model

GUI Model

Hardware Specification

Intel Pentium IV

256/512 MB RAM

1 GB Free disk space or greater

1 GB on Boot Drive

17” XVGA display monitor

1 Network Interface Card (NIC)

Software Environment

MS Windows XP/2000

MS IE Browser 6.0/later

MS Dot Net Framework 2.0

MS Visual Studio.NET 2005

MS SQL Server 2000

Language :ASP.Net(VB.NET)