Bitcoin Ransomware Payment Analysis
Author : G Anitha, Kanimozhi P and Sivavarshinika P
Abstract :
The increasing prevalence of ransomware attacks has posed a significant threat to global cybersecurity, with Bitcoin serving as the primary medium for ransom payments due to its decentralized and pseudonymous nature. This paper presents a data-driven approach to analyzing ransomware-related Bitcoin transactions. A web-based analytical dashboard is developed using Streamlit to visualize transaction patterns and detect anomalies in real time. Machine learning techniques, including Isolation Forest and Random Forest, are employed to classify transactions, identify suspicious activities, and predict high-risk payments. Furthermore, network analysis methods are utilized to uncover relationships between ransomware families and their associated Bitcoin addresses. The study also incorporates a predictive risk-scoring mechanism to assess future ransomware payment trends. The insights gained from this research provide valuable intelligence for cybersecurity experts, to enhanced detection and mitigation strategies against ransomware threats.
Keywords :
Ransomware, Bitcoin transactions, anomaly detection, machine learning, risk scoring, cybersecurity.
