A Customized Machine Learning Model for Improving Malware Detection

Abstract

Malware detection is a significant factor in establishing effective cybersecurity in the face of constantly increasing cyber threats. This research article aims to investigate the field of machine learning (ML) techniques for malware detection. More specifically, the paper focuses on the Customized K-Nearest Neighbors (C-KNN) classifier and the Firefly Algorithm (FA). The work aims to assess the effectiveness of C-KNN and C-KNN with FA (C-KNN/FA) in malware identification using the MalMem-2022 dataset. The novelty of the proposed method lies in the synergistic integration of the C-KNN algorithm with the FA for metaheuristic optimization. The use of FA to select the most relevant features enables the C-KNN to train on a small and high-quality feature set. Therefore, the performance of malware detection will be improved. We compare the performance of both methods to understand the influence of KNN parameter adjustment and feature selection on malware classification. The C-KNN and C-KNN/FA have produced remarkable results in malware identification, reaching an accuracy of 99.98%. This accomplishment is quite encouraging. With regard to multiclass and binary classification methods, C-KNN and C-KNN/FA both perform better than their alternatives.