A Malware Detection Framework Based on Forensic and Unsupervised Machine Learning Methodologies

摘要: The detection of malware intrusion requires the identification its signature. However, it is a complex task due to sophisticated ability evade security mechanisms deployed by cybersecurity practitioners. Evasion possible authors changing signature using metamorphism or polymorphism tactics. Currently, necessary formulate method focusing on dynamic and automated analysis. Malware Indicator Compromise (IOC) data analysis with machine learning can be used as technique obtain signatures. This technical approach practical cyber-attacks new changed are pandemic remain undetected, therefore, framework needed overcome this situation. Thus, research proposed based forensic unsupervised methodologies. experimented proven in detecting referring derived from Furthermore, provide guidelines for practitioners conduct threat hunting within their IT systems.