Automated Malware Detection Based on a Machine Learning Algorithm

Malware detection relies on the discriminative power of machine learning to identify new variants of malware samples. Automated malware detection, driven by machine learning algorithms, has garnered significant recognition for its capability to detect previously unknown malware. In recent years, various machine learning techniques have exhibited promise in enhancing malware detection. These techniques facilitate the analysis of substantial datasets, the identification of intricate patterns, and the detection of emergent threats, surpassing conventional signature-based methods. This paper offers an overview of these machine learning techniques and their potential to enhance the precision and efficiency of malware detection systems. The objective is to conduct a comprehensive literature review, analyze selected research papers, and present a taxonomy of machine learning methods for malware detection. The study delves into the intersection of malware and machine learning within the cybersecurity domain, encompassing the taxonomy of malware detection and the classification of machine learning algorithms. Moreover, the taxonomy is employed to evaluate the latest algorithms and perform an exhaustive analysis of machine learning approaches. Additionally, this paper discusses the challenges related to the application of machine learning in malware detection. Machine learning techniques provide a robust toolkit for the development of more effective and efficient malware detection systems. Continued research in this field is imperative to mitigate the ever-growing malware threat.