Fast Feature Reduction in intrusion detection datasets

TL;DR

This paper introduces a simple, fast feature selection method for intrusion detection datasets that reduces computational costs significantly, enabling quicker learning without substantially sacrificing accuracy.

Contribution

A novel, computationally efficient feature reduction technique for intrusion detection datasets that outperforms existing similarity-based methods in speed, with comparable accuracy.

Findings

Proposed method is much faster than existing algorithms.

Accuracy is comparable to other methods despite faster processing.

Significant reduction in computational cost achieved.

Abstract

In the most intrusion detection systems (IDS), a system tries to learn characteristics of different type of attacks by analyzing packets that sent or received in network. These packets have a lot of features. But not all of them is required to be analyzed to detect that specific type of attack. Detection speed and computational cost is another vital matter here, because in these types of problems, datasets are very huge regularly. In this paper we tried to propose a very simple and fast feature selection method to eliminate features with no helpful information on them. Result faster learning in process of redundant feature omission. We compared our proposed method with three most successful similarity based feature selection algorithm including Correlation Coefficient, Least Square Regression Error and Maximal Information Compression Index. After that we used recommended features by each of these algorithms in two popular classifiers including: Bayes and KNN classifier to measure the quality of the recommendations. Experimental result shows that although the proposed method can't outperform evaluated algorithms with high differences in accuracy, but in computational cost it has huge superiority over them.