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MalCure: Smart Malware Response System Using CVE Intelligence

K. Sabitha, S. Padalingam, C. Prakash, G. Sangameshwaran

Abstract


This paper presents a data-driven, intelligent Vulnerability Assessment and Common Vulnerabilities and Exposures (CVE) Intelligence System that will detect, analyze, and remediate security vulnerabilities in computing environments. The proposed system begins by conducting full system-wide vulnerability scans and cross-referencing the vulnerable components detected with the CVE database to find any relevant documentation of vulnerabilities and remediation options associated with those CVE entries. When a relevant CVE demonstration is located, the system will automatically apply the recommended patch or mitigation to secure the vulnerable component or system. When no CVE documentation is located, these vulnerabilities are recorded as zero-day, triggering an alert and generating system logs for analysis and follow-up. The framework also includes system–wide file scanning to identify outdated or insecure files and to ensure system-wide protection. Combining automated vulnerability detection, actionable CVE-based remediation, and zero-day recognition, the proposed framework provides a proactive, adaptive, and efficient method of enhancing cybersecurity resilience that will be used both for academic purposes and in practice.


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References


Maniriho, Pascal & Mahmood, Abdun & Chowdhury, Mohammad. (2024). MeMalDet: A Memory analysis-based Malware Detection Framework using deep autoencoders and stacked ensemble under temporal evaluations. Computers & Security. 142. 103864. 10.1016/j.cose.2024.103864.

Mohamed, Nachaat. (2025). A comprehensive framework for cyber threat detection: leveraging AI, NLP, and malware analysis. International Journal of Information Technology. 10.1007/s41870-025-02466-4.

Abdeen, Basel & Al-Shaer, Ehab & Singhal, Anoop & Khan, Latifur & Hamlen, Kevin. (2024). SMET: Semantic mapping of CTI reports and CVE to ATT&CK for advanced threat intelligence. Journal of Computer Security. 1-20. 10.3233/JCS-230218.

Hu, Wanyu & Thing, Vrizlynn. (2024). CPE-Identifier: Automated CPE identification and CVE summaries annotation with Deep Learning and NLP. 10.48550/arXiv.2405.13568.

Eisenwer, Sergio & Berenyi, Stefano & Zaharoff, Anton & Montrose, Julian & Solberg, Emmanuel & Grimaldi, Fergus. (2024). Automated Detection of Ransomware Using Dynamic Code Sequence Mapping. 10.36227/techrxiv.173014814.48823875/v1.

Ekanem, Akpasam & Bassey, Samuel. (2024). Threats, Detection, Prevention, And Recovery Techniques For Ransomware.

E. P. Torres P. and S. G. Yoo, “Detection and neutralizing encrypting attacks by Using Ransomeware machine-learning techniques: A literature review,’ Int. J. Appl. Eng. Res., Vol. 12, no 18, pp. 7902 7911, 2017.

H. Jivisar, J. Benson, and A. Kowalski, “Automated detection of compressed and encrypted ransomware data using a three-layer convolutional neural network,” 2024.

H. Monota and Y. Shigeta, “Optimizing alignment with progressively selective weight enhancement in large language models,” 2024.

M. Ozturk, A. Demir, Z. Arslan, and O. Caliskan, “Dynamic behavioural analysis of privacy-breaching and data theft ransomware,” 2024.


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