Ransomware Detection Using Machine Learning

Abstract

Ransomware has emerged as one of the most
disruptive and financially damaging forms of
cybercrime, capable of paralyzing individuals,
organizations, and critical infrastructure by
encrypting sensitive data and demanding ransom
payments. This seminar report explores the evolving
landscape of ransomware attacks, focusing on the
rise of Ransomware-as-a-Service (RaaS), double
extortion tactics, and advanced evasion techniques.
Traditional detection methods, primarily signaturebased,
are increasingly ineffective against modern,
polymorphic ransomware variants. In response, the
integration of machine learning (ML) techniques
offers a promising direction for enhancing
ransomware detection capabilities. This study
investigates various ML models—supervised,
unsupervised, and deep learning—and their
applications in detecting ransomware during
different stages of its lifecycle, including delivery,
execution, and command-and-control
communication. Emphasis is placed on feature
engineering, real-time detection challenges, and the
need for high-quality, standardized datasets. The
report also outlines research limitations and future
directions, advocating for scalable, interpretable,
and proactive ML-based defense mechanisms.
Through a comprehensive analysis, this work
highlights the potential of machine learning to
revolutionize ransomware detection and fortify
cyber resilience.