PROJECT TITLE :

Constructing important features from massive network traffic for lightweight Intrusion Detection

ABSTRACT:

Efficiently processing large information may be a big issue in high-speed Network Intrusion Detection, as network traffic has become increasingly giant and complicated. In this work, rather than constructing a giant variety of features from massive network traffic, the authors aim to pick out the foremost important features and use them to detect intrusions in a very quick and effective manner. The authors 1st used several techniques, that is, information gain (IG), wrapper with Bayesian networks (BN) and Decision trees (C4.5), to pick necessary subsets of options for network intrusion detection based on KDD'99 data. The authors then validate the Feature Selection schemes during a real network test bed to detect distributed denial-of-service attacks. The feature choice schemes are extensively evaluated primarily based on the two data sets. The empirical results demonstrate that with only the most necessary 10 features selected from all the original forty one features, the attack detection accuracy almost remains the same or perhaps becomes better primarily based on both BN and C4.five classifiers. Constructing fewer options will additionally improve the efficiency of network intrusion detection.