Bridging Static and Dynamic Frontiers: A Systematic Review of Malware Detection Techniques

Malware is malicious software designed to disrupt, damage, or gain unauthorized access to computer systems, networks, and data. Static and dynamic analysis are two foundational techniques for identifying malware, with static analysis examining code without execution and dynamic analysis observing behavior during runtime. This systematic literature review (SLR) evaluates the strengths and limitations of each method by analyzing 30 peer-reviewed studies published between 2019 and 2024. Our synthesis identifies a recurring theme in the literature: a shift toward hybrid detection models that integrate the complementary advantages of both approaches. These strategies aim to address the constraints of individual techniques, such as the difficulty of static analysis with obfuscated code and the resource demands of dynamic analysis, thus improving detection robustness and accuracy. This review outlines key evaluation criteria, compares methodological trade-offs, and highlights research gaps. We conclude by advocating for further refinement and validation of hybrid methodologies to meet the demands of evolving cybersecurity threats.