Feature Selection-Based Machine Learning Model for Malware Detection

Identifying malware is a critical task to ensure computer system’s security, and machine learning algorithms have shown magnificent performance in this area due to their ability of learning patterns from large datasets. However, the quality and relevance of features used in the model can negatively affect the performance of machine learning algorithms. In this paper, we traverse the effectiveness of six different machine learning algorithms, namely, logistic regression, decision tree, Naïve Bayes, SVM, random forest, and k-nearest neighbor, for identifying malware along with feature selection including dragonfly optimization (DFO) and particle swarm optimization (PSO). We evaluate these algorithms on a memory-based balanced dataset containing a mix of benign and malicious files and then examine their performance in terms of accuracy, precision, recall, and F1-score. Our experimental results show that the dragonfly feature selection technique achieves the highest overall performance. The K-nearest neighbor algorithm outperforms the other algorithms by achieving accuracy of 99.97%. Our findings suggest that incorporating feature selection techniques can improve the performance of machine learning algorithms for malware detection, and the dragonfly technique provides the best results.