Abstract

In our digitally interconnected world, malware poses a persistent threat to information security. This paper explores automated malware analysis as a vital component of cybersecurity, offering a comprehensive overview of its fundamental concepts, methodologies, and contributions to combating malicious software. The study focuses on core principles, examining static and dynamic analysis techniques, behavioral analysis, and signature-based detection methods. It evaluates their strengths and limitations, emphasizing their relevance in addressing diverse malware threats. Results indicate the effectiveness of both static and dynamic analysis, with dynamic analysis showing slightly better performance. Combining these techniques enhances detection accuracy, particularly for polymorphic malware. The conclusion underscores the promise of automated malware analysis in signature-based detection and suggests future research directions for improving effectiveness and efficiency.