Detection of malware attacks in memory dump system by using machine learning technologies

As cyber threats continue to evolve in complexity, memory dump analysis has emerged as a critical technique for detecting sophisticated malware attacks. This research presents an advanced framework for the detection of malware embedded within memory dumps using machine learning technologies. By leveraging both supervised and unsupervised learning models, the proposed approach identifies malicious patterns that may evade traditional signature-based detection methods. Features are extracted from raw memory dumps using a combination of dynamic analysis and feature engineering techniques, enabling the classifiers to distinguish between benign and malicious behaviors with high accuracy. Experimental results on benchmark datasets demonstrate the effectiveness of this methodology, achieving improved detection rates and reduced false positives. This study highlights the potential of intelligent systems in enhancing digital forensics and strengthening cybersecurity defense.