A Systematic Review and Comparative Analysis of Malware Detection Techniques

Abstract

The increasing sophistication and volume of malware pose a persistent and evolving threat to cybersecurity. The research paper systematically examines and compares various malware detection techniques, including traditional methods such as signature, heuristic, and anomaly detection, and more advanced methods such as behavior analysis, sandboxing, and machine learning, including deep learning. The study examined the mechanisms, advantages, and disadvantages of each technique using recent empirical data from academic literature. A comprehensive table provides a parallel comparison of these methods based on key performance indicators, efficacy against evasive malware, and resource consumption. In addition, it discusses the current challenges of malware detection, such as the increasing complexity of malware, evasion tactics, and threats to machine learning models. Finally, it explores emerging trends and future directions in this field, including integration of artificial intelligence, cloud analysis, proactive defense mechanisms, and the growing role of large language models. This review underscores the need to continuously innovate and adapt malware detection strategies to effectively counter the evolving landscape of cyber threats.