Systematic Literature Review on the Application of Blockchain in Enhancing Server Security: Research Methods for Mitigating Ransomware and Malware Attacks
DOI:
https://doi.org/10.62951/ijcts.v1i4.186Keywords:
Blockchain, Server Security, Ransomware, Malware, Attack Mitigation, Smart Contracts, IDS, Critical InfrastructureAbstract
This study aims to explore the application of blockchain in enhancing server security to mitigate ransomware and malware attacks in critical infrastructures such as healthcare, finance, and government sectors. Using a systematic literature review (SLR) approach, the research collects articles from four major databases (IEEE Xplore, Scopus, ScienceDirect, and SpringerLink) published between 2020 and 2024. The search focuses on keywords related to blockchain, server security, ransomware, malware, and attack mitigation. The results indicate that blockchain enhances data integrity, transaction security, and strengthens access control to protect sensitive data. Moreover, integrating blockchain with intrusion detection systems (IDS) and using smart contracts accelerates threat detection and response, allowing for automatic blocking and data recovery from attacks. This technology reduces reliance on manual intervention and increases operational efficiency. However, the main challenges in its implementation include high implementation costs, scalability, and technical complexity. Nevertheless, blockchain offers significant solutions for mitigating ransomware and malware attacks while enhancing the reliability and efficiency of systems. In conclusion, blockchain provides an effective solution for server security and cyber threat mitigation, although challenges related to cost and scalability need to be addressed. Further research is required to develop more efficient blockchain protocols and integrate them with other technologies to enhance threat detection and response speed.
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Rehman, Z., Gondal, I., & Ge, M. (2024). Proactive defense mechanism: Enhancing IoT security through diversity-based moving target defense and cyber deception. Computers & Security, 139, 103685. https://doi.org/10.1016/j.cose.2024.103685
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Li, X., & Chen, T. (2020). Distributed Ledger for Malware Detection. Future Generation Computer Systems, 136, 101–120.
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Lin, W., & Zhang, Y. (2022). Leveraging Blockchain for Malware Detection in Critical Systems. Journal of Strategic Information Systems, 29(4), 211–235.
McIntosh, T.R., Susnjak, T., & Liu, T. (2024). From COBIT to ISO 42001: Evaluating cybersecurity frameworks for opportunities, risks, and regulatory compliance. Computers & Security, 144, 103964. https://doi.org/10.1016/j.cose.2024.103964
Marais, J., Potgieter, P., & Naidoo, R. (2022). AI-based malware detection models for ransomware prevention. Computers & Security, 134, 103541. https://doi.org/10.1016/j.cose.2022.103541
Ramos-Cruz, B., Andreu-Perez, J., & Martínez, L. (2024). The cybersecurity mesh: A comprehensive survey of involved artificial intelligence methods, cryptographic protocols, and challenges for future research. Neurocomputing, 581, 127427. https://doi.org/10.1016/j.neucom.2024.127427
Rustam, F., & Jurcut, A.D. (2024). Malicious traffic detection in multi-environment networks using novel S-DATE and PSO-D-SEM approaches. Computers & Security, 136, 103564. https://doi.org/10.1016/j.cose.2024.103564
Zhang, T., & Qiao, Y. (2021). Blockchain-based Intrusion Detection System. IEEE Transactions on Cybersecurity, 32(1), 45–56.
Zhuang, X., & Zhou, M. (2023). Blockchain-driven Security Enhancements for IoT Systems. Springer Transactions on Security, 42(6), 250–265.
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