Malware Analysis and Detection Techniques

As the digital landscape evolves, the sophistication and frequency of malware attacks have escalated, necessitating advanced methodologies for their detection, analysis, and defence. This research delves into state-of-the-art techniques and innovative strategies that are shaping the future of malware combat. We explore the application of deep learning and artificial intelligence in identifying and classifying malware, emphasizing the role of adversarial machine learning in enhancing detection resilience. Behavioural and dynamic analysis methods are scrutinized to uncover malware patterns during execution, alongside the development of countermeasures against sandbox evasion tactics. The study extends to advanced static analysis and automated reverse engineering techniques aimed at expediting the identification of malicious code.