Abstract

This review provides a comprehensive analysis of signature-based malware detection. It defines the fundamental mechanics from cryptographic hashes to complex byte-pattern rules, integration into tools like ClamAV, Snort, and YARA, and commercial Endpoint Protection Platforms. A central focus is the critical evaluation of the methodology’s strengths like speed, high accuracy for known threats, and low computational overhead, and its well-documented weaknesses, particularly its inability to detect zero-day exploits and polymorphic malware. The paper delves into adversarial evasion techniques that are designed explicitly to defeat signature-based controls. Subsequently, the paper situates signature-based detection within the modern security paradigm, examining its role as a component in hybrid architectures that incorporate heuristic, behavioral, sandboxing, and AI/machine learning techniques. The paper concludes that signature-based detection still remains an indispensable and highly efficient first-line filter in a layered, defense-in-depth strategy, with its future lying in AI-based automation and threat intelligence.