Ransomware Detection Dynamics: Insights and Implications
arXiv:2402.04594 · doi:10.12785/ijcds/150165
Abstract
The rise of ransomware attacks has necessitated the development of effective strategies for identifying and mitigating these threats. This research investigates the utilization of a feature selection algorithm for distinguishing ransomware-related and benign transactions in both Bitcoin (BTC) and United States Dollar (USD). Leveraging the UGRansome dataset, a comprehensive repository of ransomware related BTC and USD transactions, we propose a set of novel features designed to capture the distinct characteristics of ransomware activity within the cryptocurrency ecosystem. These features encompass transaction metadata, ransom analysis, and behavioral patterns, offering a multifaceted view of ransomware-related financial transactions. Through rigorous experimentation and evaluation, we demonstrate the effectiveness of our feature set in accurately extracting BTC and USD transactions, thereby aiding in the early detection and prevention of ransomware-related financial flows. We introduce a Ransomware Feature Selection Algorithm (RFSA) based on Gini Impurity and Mutual Information (MI) for selecting crucial ransomware features from the UGRansome dataset. Insights from the visualization highlight the potential of Gini Impurity and MI-based feature selection to enhance ransomware detection systems by effectively discriminating between ransomware classes. The analysis reveals that approximately 68% of ransomware incidents involve BTC transactions within the range of 1.46 to 2.56, with an average of 2.01 BTC transactions per attack. The findings emphasize the dynamic and adaptable nature of ransomware demands, suggesting that there is no fixed amount for specific cyberattacks, highlighting the evolving landscape of ransomware threats.
References in corpus (11)
- Using ARIMA to Predict the Expansion of Subscriber Data Consumption
- Ransomware Analysis using Feature Engineering and Deep Neural Networks
- Zero-Day Threats Detection for Critical Infrastructures
- Enhancing search engine precision and user experience through sentiment-based polysemy resolution
- Smart Sentiment Analysis-based Search Engine Classification Intelligence
- Assessing Cyclostationary Malware Detection via Feature Selection and Classification
- MALIGN: Explainable Static Raw-byte Based Malware Family Classification using Sequence Alignment
- Navigating the complex nexus: cybersecurity in political landscapes
- Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM
- Requirements for a Career in Information Security: A Comprehensive Review
- Cybersecurity Career Requirements: A Literature Review