Proactive Ransomware Detection & Mitigation Using Machine Learning

Abstract

The ransomware attack is one of the most prominent forms of cybersecurity risks, as it can cause the crucial information unavailable and cause significant harm to the functioning of critical services within various sectors. The conventional techniques of signature-based detections have been proven highly ineffective when dealing with sophisticated attacks involving code obfuscations, polymorphism, and covert transmission strategies. Therefore, this paper offers machine learning (ML) as an effective tool for identifying ransomware, especially those involving static feature analysis methods. The methods presented below provide an insight into non-executable analyses to observe at the properties of a file, such as byte distributions, entropy values, executables, and instruction sets, without running malware. The study involves a thorough comparison of such ML classifiers as Support Vector Machines (SVM), Random Forest (RF), and boosted algorithms on their performance, efficiency, and versatility. Some of the major classical problems addressed by the paper are the false positives problem, the non-reality of deployment, and data imbalance. Other key factors included are the importance of mitigation-aware approaches, pre-attack detection mechanisms, and a combination of static and dynamic analysis frameworks. On the whole, the paper clearly demonstrates the potential offered by machine learning powered static analysis for the development of efficient future ransomware protection systems.