Malicious uses of blockchains by malware: from the analysis to Smart-Zephyrus

Gimenez-Aguilar, Mar; Fuentes, José María de; González-Manzano, Lorena

doi:10.1007/s10207-023-00700-3

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…They have explored various implementation and security-related issues also that may affect the implementation of blockchain. A lot of secure mechanisms have also been explored by researchers to mitigate these issues [17][18][19][20][21][22][23][24][25][26][27]. To provide a secured blockchain infrastructure, in this section, a thorough analysis has been performed on the existing literature to identify the various security issues and their possible solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing blockchain security: a novel approach to integrated malware defence mechanisms

Sharma,

Upadhyay,

Sharma

2024

Eng. Res. Express

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This paper introduces a novel integrated hybrid malware attack detection algorithm, focusing on enhancing cybersecurity within blockchain systems by addressing the prevalent challenges of Byzantine fault tolerance, Reentrancy, and DDOS attacks.The significance of this research lies in its contribution to safeguarding blockchain technology, a cornerstone for secure,decentralized digital transactions, against sophisticated malware threats. Current cybersecurity solutions frequently fall short of offering a complete defense mechanism, making it difficult to effectively combat a variety of dynamic malware attacks atthe same time. Thus, the main objective of this research is to provide a hybrid framework that combines DDOS attack prevention, reentrancy attack detection, and Byzantine fault tolerance detection into a single, cohesive architecture. Theproposed hybrid framework encompasses a detailed algorithmic approach integrating SHA-256 and DSA to analyze theaforementioned three malware attacks. A hybrid model combining these algorithms, implemented in one block, has been developed to mitigate malicious activity. These measures aim to improve computational complexity and expedite execution within the network of nodes. To test the efficacy of the proposed framework, the approach is tested on the NSL-KDD dataset to analyze the malicious activities. The performance analysis of the proposed frameworks presents a recall and F1 score of .73 and .68 respectively. Furthermore, for efficient mitigation, the time and space complexity analysis are performed on proposed algorithms for attack analysis, which resulted in a combination of constant and linear time complexity operations. The findings reveal that the proposed algorithm successfully identifies and mitigates the targeted malware attacks and maintains optimal performance in terms of time and space complexity. This research's contribution to the cybersecurity field is significant, offering a robust, scalable solution that enhances the resilience of blockchain networks against a broad spectrum of malware attacks.

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