Malware Detection using Artificial Bee Colony Algorithm
This work addresses the challenge of efficient and universal malware detection for cybersecurity analysts by tackling the curse of dimensionality in feature selection.
This paper proposes a malware detection algorithm that uses the Artificial Bee Colony (ABC) evolutionary algorithm for feature selection. The method aims to reduce feature dimensions, thereby accelerating the malware detection process and outperforming state-of-the-art approaches.
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.