Blockchain security for ransomware detection (2407.16862v1)

Abstract: Blockchain networks are critical for safeguarding digital transactions and assets, but they are increasingly targeted by ransomware attacks exploiting zero-day vulnerabilities. Traditional detection techniques struggle due to the complexity of these exploits and the lack of comprehensive datasets. The UGRansome dataset addresses this gap by offering detailed features for analysing ransomware and zero-day attacks, including timestamps, attack types, protocols, network flows, and financial impacts in bitcoins (BTC). This study uses the Lazy Predict library to automate ML on the UGRansome dataset. The study aims to enhance blockchain security through ransomware detection based on zero-day exploit recognition using the UGRansome dataset. Lazy Predict streamlines different ML model comparisons and identifies effective algorithms for threat detection. Key features such as timestamps, protocols, and financial data are used to predict anomalies as zero-day threats and to classify known signatures as ransomware. Results demonstrate that ML can significantly improve cybersecurity in blockchain environments. The DecisionTreeClassifier and ExtraTreeClassifier, with their high performance and low training times, are ideal candidates for deployment in real-time threat detection systems.

• The paper demonstrates that DecisionTreeClassifier and ExtraTreeClassifier can achieve near-perfect accuracy with minimal computational overhead for ransomware detection in blockchain networks.
• It introduces the UGRansome dataset, which provides detailed features such as timestamps, protocols, and financial data to analyze zero-day ransomware attacks.
• The study emphasizes balancing model performance and efficiency, paving the way for practical, real-time threat detection and advanced blockchain security.

Blockchain Security for Ransomware Detection

The paper explores an innovative approach to enhancing blockchain security by utilizing ML techniques to detect ransomware across zero-day attacks. Blockchain networks, heralded for their robust security and transparency, nonetheless remain vulnerable to sophisticated threats such as ransomware and zero-day exploits. Traditional methods struggle to address these challenges due to the lack of comprehensive datasets and the inherent complexity of unknown vulnerabilities. The introduction of the UGRansome dataset addresses this gap, providing detailed attributes crucial for the analysis and anticipation of complex threats.

Methodological Approach

The paper employs the Lazy Predict library to streamline model selection and evaluation, leveraging the UGRansome dataset's rich features, including timestamps, protocols, and financial data. This automated process facilitates rapid comparison across various ML models, enabling the identification of algorithms best suited for detecting anomalies and classifying known ransomware activities. The DecisionTreeClassifier and ExtraTreeClassifier were highlighted for their high accuracy and low training times, making them ideal candidates for real-time applications.

Results and Implications

Numerical results from Lazy Predict demonstrate the models' potential in improving cybersecurity within blockchain environments. Notably, the DecisionTreeClassifier and ExtraTreeClassifier achieved near-perfect accuracy with minimal computational overhead, suggesting their viability for deployment in scenarios requiring swift threat detection and response. Conversely, models like NuSVC, while accurate, are impractical for real-time applications due to prohibitive training times.

The paper underscores the importance of balancing performance and efficiency when selecting ML models for ransomware detection. The real-time detection capabilities provided by ensemble learning techniques, such as the high correlation between BTC transactions and ransomware activity, signify a substantial advancement in the proactive defense of blockchain security.

Theoretical and Practical Implications

From a theoretical standpoint, the research advances the understanding of feature selection in threat detection models, suggesting that comprehensive datasets like UGRansome are instrumental in evolving blockchain security strategies. Practically, the application of models such as DecisionTreeClassifier and ExtraTreeClassifier offers a feasible solution for real-time threat defense, highlighting the critical role of machine learning in modern cybersecurity frameworks.

Future Directions

Future research could focus on expanding the UGRansome dataset and refining model selection to enhance detection accuracy further. Additionally, exploring ROC AUC metrics and other performance indicators could provide deeper insights into model capabilities, particularly in distinguishing zero-day exploits.

In conclusion, this paper paves the way for enhanced blockchain security through strategic machine learning applications, showcasing the potential to significantly mitigate threats in an evolving digital landscape. Through robust dataset features and efficient model implementations, proactive threat detection in blockchain environments appears more attainable, promising a fortified future in cybersecurity research.