Analytical Study of Intruder Detection System in Big Data Environment

Abstract

This paper presents analytical study of intruder detection system in big data environment. In recent years, the size of data increases at high speed, through daily increasing in number of people and online applications such as online airline ticket booking, online banking, online payment system, etc., using the Internet and network services, and this leads to a huge amount of data from terabyte to petabytes which are called as the big data. Therefore, predict and analysis of network traffic from a possible intrusion attack through permanent gathering of network traffic data and learning of their characteristics on the fly are the critical aspects. Various experiments are conducted and summed up, to be able to identify different problems in existing network applications and traffics. Analysis of intruder detection over network traffic includes support vector machine (SVM) approach, distance-based classifiers, genetic algorithm, and fuzzy neural network classifiers based on data mining techniques. Similarly, several interesting hybrid techniques are implemented to attain efficient and effective results of intruder detection system analysis over network.