Efficient Method for Intrusion Detection and Classification and Compression of Data

Abstract

Computers and Smartphone's becomes vital part of everyday life and hence use of internet becomes more and more. Due to internet, computers are becomes vulnerable of different kinds of security threats. Therefore it is required that we need to have efficient security method in order to avoid leakage of important data or misuse of data. This security method is called as Intrusion Detection System (IDS). Since from last two decades IDS becomes core area of many researchers and many methods are already presented for efficient intrusion detection and classification. Most of methods are out dated as many new attacks generated by hackers. In this project our main aim is to presented scalable and efficient method for intrusion detection and classifications. For intrusion detection, we are not using traditional methods, rather we are focusing on using distributed approach, which not only improves the scalability but also improves efficiency. Proposed method is divided into two parts, detection and classification. Attack graph is constructed followed by preprocessing. Attack graph approach is work on modeling the input flows by extracting their features. We have done classification on online data and found attacks. Experimental results shows that the performance of our system is better than the traditional one.