A Study on Next-Generation Malware Reverse Engineering Paradigms

As cyber threats evolve, complex malware targets various systems, weak points in a wide range of situations and can't be stopped by conventional measures. To address these emerging and potent threats, this paper examines the next-generation malware reverse engineering paradigms. We discuss how AI/ML is applicable to the topic for anomaly detection, hypervisor-based introspection to secure continuous monitoring, memory analysis for file-less malware, and system calls mapping that helps in tracking the execution flow of malware. Measures, which allow countering modern malware's features such as obfuscation, polymorphism, and encrypted messages, are described, and they include methods like a hybrid approach to threat analysis, adaptable sandboxes, and heuristic modeling. Some of these techniques have been showcased herein with real-life examples captured in the enterprise and industrial control systems domains. Prevention techniques including the use of prediction models and automatic response mechanisms to control threats are presented to improve threat detection and mitigation. Lastly, in the area of future perspectives for malware detection and analysis, the research looks at quantum-resistant security features, the adoption of blockchain technologies for safe threat sharing as well as progress in cognitive computing for supporting decision-making processes. Such emerging paradigms are emblematic of the need for constant change to preserve a high-security level as the threats keep growing much more severe in the present day context.