Enhancing malware detection utilizing Chi-Square distribution for optimal feature selection in machine learning black box models

Abstract

Malware detection is an escalating challenge, prompting researchers to propose numerous solutions leveraging various techniques as crucial components in combating malware. Intrusion System Detection (IDS) assumes a central role in fortifying security and is deemed an essential defense strategy for every infrastructure. However, regardless of the IDS method employed, novel challenges persist, including the imperative for minimal CPU time, indicative of the IDS response time. In this paper, we present an optimal Black Box IDS based on machine learning classifiers and obfuscated features. This streamlined approach incorporates an innovative algorithm that reduces the feature set using the Chi-Square distribution, achieving maximum accuracy and minimal CPU time simultaneously. The proposed IDS implementation involves four machine-learning classifiers, namely Naïve Bayes, Support Vector Machines, K-Nearest Neighbors, and Random Forest. Our tests demonstrated the efficiency of the proposed approach, effectively minimizing features to a crucial set of five, attaining an impressive accuracy rate of 99.90% within a CPU time corresponding to 0.0017 seconds. This outcome underscores the efficiency of our approach in significantly reducing the feature set while maintaining high accuracy, highlighting its potential for effective implementation in practical scenarios.