| Abstrakt: |
The threat of malicious software has evolved into a major concern regarding the security of the system and network infrastructure. Machine learning algorithms have been successfully utilized to classify malware files into malicious or benign. However, the exponential growth in data volume and feature dimensionality poses challenges for machine learning, resulting in reduced classification accuracy and heightened computational costs. Feature selection is an essential process that can address these challenges by eliminating irrelevant, redundant, and less informative features that may adversely affect classifier performance. In this study, we introduce an enhanced Whale Optimization Algorithm (EWOA) aimed at improving classification accuracy, feature selection, and overall malware detection model efficiency. The proposed EWOA introduces an enhanced search mechanism that integrates mutation and neighborhood search strategies, aiming to refine its exploration strategy. This novel approach is more adept at steering clear of local optima. Additionally, EWOA augments its population diversity by incorporating the Opposite-Based Learning technique (OBL) during its initial phase. To assess the efficacy of the proposed method, performance evaluations were conducted using the CIC-MalMem-2022 dataset. Various aspects including the number of features, efficiency, fitness value, accuracy, and statistical tests were compared across different optimization algorithms: Gray Wolf Optimization Algorithm (GOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Lion Optimization (ALO), Butterfly Optimization Algorithm (BOA), and Slime Mould Algorithm (SMA). The experimental results affirm the superiority of EWOA over other optimization algorithms in diverse areas, such as classification accuracy (99.987%), fitness value (0.00084511%), and average feature count (on average, 3.97 features). [ABSTRACT FROM AUTHOR] |
