Malware Detection using Artificial Bee Colony Algorithm

TL;DR

This paper introduces a malware detection method utilizing the Artificial Bee Colony algorithm for feature selection, effectively reducing feature dimensions and improving detection performance in real-time scenarios.

Contribution

It proposes a novel feature selection approach using ABC to enhance malware detection efficiency and accuracy, addressing the curse of dimensionality.

Findings

Outperforms existing malware detection methods

Reduces feature dimensions significantly

Improves detection speed and accuracy

Abstract

Malware detection has become a challenging task due to the increase in the number of malware families. Universal malware detection algorithms that can detect all the malware families are needed to make the whole process feasible. However, the more universal an algorithm is, the higher number of feature dimensions it needs to work with, and that inevitably causes the emerging problem of Curse of Dimensionality (CoD). Besides, it is also difficult to make this solution work due to the real-time behavior of malware analysis. In this paper, we address this problem and aim to propose a feature selection based malware detection algorithm using an evolutionary algorithm that is referred to as Artificial Bee Colony (ABC). The proposed algorithm enables researchers to decrease the feature dimension and as a result, boost the process of malware detection. The experimental results reveal that the proposed method outperforms the state-of-the-art.