Multi-objective Markov-enhanced Adaptive Whale Optimization Cybersecurity Model for Binary and Multi-Class Malware Cyberthreat Classification

The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger; the real threat lies in the obfuscation of these cyberattacks, as they constantly change their behavior, making detection more difficult. Numerous researchers and developers have devoted considerable attention to this topic; however, the research field has not yet been fully saturated with high-quality studies that address these problems. For this reason, this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization (MOMEAWO) cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach. The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing, detecting, and classifying the behavior of obfuscated malware within their respective families. The proposed model includes three classification types: binary classification and multi-class classification (e.g., four families and 16 malware families). To evaluate the performance of this model, we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis (CIC-MalMem-2022) that contains balanced data. The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.