Machine Learning for Analysing Malware and Ransomware
DOI:
https://doi.org/10.15662/IJEETR.2026.0802174Keywords:
Machine Learning, Malware Detection, Ransomware Analysis, Supervised Learning, Adaptive Learning, Dynamic Analysis, Feature Extraction, Static Analysis, Zero-Day AttacksAbstract
Malware and ransomware are rapidly evolving, making detection difficult for traditional signature-based security systems that rely on known attack patterns. These systems often fail to identify new or zero-day threats. This research proposes an intelligent machine learning–based detection system that uses supervised models and deep learning techniques to identify malware in real time with high accuracy and low false positives. The system combines static and dynamic feature extraction to analyse file structure and runtime behaviour. It also includes ransomware-specific behavioural analysis. A hybrid adaptive learning technique with feature fusion and incremental model updating helps the system adapt to changing malware patterns, improving detection performance and system security
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