2022
DOI: 10.25271/sjuoz.2022.10.1.865
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Bitcoin Ransomware Detection Employing Rule-Based Algorithms

Abstract: Cryptocurrencies have completely altered the digital transaction process all over the globe. Almost a decade after Satoshi Nakamoto generated the first Bitcoin block; many cryptocurrencies have been established. The Ransomware attack is a type of cybercrime and a class of malware that encrypts the files and prevents users from accessing their data or systems and demands payment for decrypting and retrieving access to their files. Ransomware data classification using present data mining and machine learn… Show more

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Cited by 7 publications
(5 citation statements)
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“…TP Rate TP / (TP+FN) [40] FP Rate FP / (FP+TN) [40] Precision TP/ (TP+ FP) [41] Recall TP/ (TP+ FN) [41] F-Measure…”
Section: Metrics Equations Referencesmentioning
confidence: 99%
“…TP Rate TP / (TP+FN) [40] FP Rate FP / (FP+TN) [40] Precision TP/ (TP+ FP) [41] Recall TP/ (TP+ FN) [41] F-Measure…”
Section: Metrics Equations Referencesmentioning
confidence: 99%
“…The proposed approach used TDA to analyze the topological structure of the Bitcoin blockchain and identify patterns that are indicative of ransomware attacks. Talabani et al in [19] outlined the design and implementation of a rule-based detection system that could analyze network traffic, file metadata, and other indicators to identify known ransomware strains and prevent their execution. The proposed approach leveraged existing knowledge about ransomware behavior and Bitcoin payment transactions to develop rules and conditions that could be used to identify and block ransomware attacks.…”
Section: Related Workmentioning
confidence: 99%
“…However, a single algorithm may not provide accurate results and may not generalize well to new or unseen data. Additionally, the authors in [19], [28] developed an efficient system for detecting money laundering in cryptocurrency transactions using ML techniques like SNN and DT, but their classification accuracy decreases as input features increase and are prone to overfitting.…”
Section: Problem Statementmentioning
confidence: 99%
See 1 more Smart Citation
“…Talabani and Abdulhadi (2022) proposed two rule-based models to address the low accuracy of ransomware detection tools which use data mining and machine learning techniques. The models known as Partial Decision Tree (PART) and Decision Table were applied to bitcoin dataset consisting of 61,004 samples of 29 ransomware families with ten descriptive and decision attributes.…”
mentioning
confidence: 99%