Malware Analysis in Threat Intelligence and Cyberattack Prediction
Keywords:
Behavioral analysis, Cuckoo Sandbox, Cyberattack prediction, Cybersecurity, Dynamic analysis, Ghidra, IDA Pro, Indicators Of Compromise (IOCs), Malware analysis, Reverse engineering, Sandbox, Static analysis, Tactics, techniques, and procedures (TTPs), Threat intelligence, Threat modellingAbstract
Malware remains a primary tool used by cybercriminals and nation-state actors in the execution of sophisticated cyberattacks. As threats evolve in complexity and scale, understanding the behavior and structure of malware becomes essential for developing proactive defence strategies. This paper examines the crucial role of malware analysis in enhancing both threat intelligence and cyberattack prediction. By applying static and dynamic analysis techniques, cybersecurity professionals can uncover valuable indicators of compromise (IOCs) and tactics, techniques, and procedures (TTPs) that inform both immediate threat detection and long-term strategic planning. This study collected a dataset of 15,000 malware and benign samples (10,000 malicious + 5,000 benign) and built a machine learning model achieving 82 % accuracy, 78 % precision, 85 % recall, and 81 % F1-score. It discusses commonly used tools such as IDA Pro, Ghidra, and Cuckoo Sandbox, and highlights how malware analysis contributes to real-time adversary detection and future attack forecasting. The integration of malware analysis into threat intelligence frameworks significantly improves the ability of organisations to detect, understand, and predict cyber-threats.
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