A Hybrid Framework Combining AI and Graph Theory for Advanced Malware Propagation Detection
Keywords:
Artificial Intelligence, Malware Detection, Graph TheoryAbstract
The increasing sophistication of malware and its ability to spread rapidly across networks poses significant challenges to cybersecurity. Traditional malware detection systems often struggle to keep up with the evolving tactics used by malicious actors. This paper presents a hybrid framework that combines Artificial Intelligence (AI) and Graph Theory to detect advanced malware propagation in real-time. The framework leverages machine learning (ML) algorithms for pattern recognition and graph theory for analyzing the relationships between network nodes, allowing for the identification of abnormal behavior indicative of malware spread. By integrating AI with graph-based models, the system can dynamically adapt to new threats, improving the detection of previously unknown malware strains. The paper also explores various case studies where the framework has been successfully implemented, discusses the strengths and limitations of this approach, and outlines future directions for enhancing malware detection in complex networks.
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References
Smith, J., & Roberts, L. (2023). Artificial Intelligence techniques for malware detection. Journal of Cybersecurity Research, 18(2), 45-62.
Lee, M., & Kim, S. (2022). Machine learning for anomaly detection in malware analysis. Cyber Threat Intelligence, 10(1), 55-72.
Kumar, R., & Sharma, P. (2021). Reinforcement learning for proactive malware detection. International Journal of AI and Security, 5(3), 150-167.
Zhang, H., & Liu, Y. (2022). Malware propagation modeling using Graph Theory. Network Security Journal, 14(2), 134-145.
Turner, A., & Greene, F. (2023). Predicting malware spread using network graph analysis. Cybersecurity Research Insights, 19(3), 204-219.
Patel, A., & Desai, R. (2023). Graph-based detection of command and control servers in malware networks. International Journal of Network Security, 16(1), 123-138.
Lin, T., & Huang, C. (2021). Integrating AI and Graph Theory for advanced malware detection. Journal of AI and Network Security, 9(4), 45-59.
Gao, Z., & Cheng, Q. (2022). Hybrid machine learning and graph-based framework for real-time malware detection. Cyber Defense Review, 13(5), 232-245.
Williams, J., & Brown, L. (2023). Detecting encrypted malware traffic using AI and Graph Theory. Digital Security Review, 22(4), 118-130.
Singh, V., & Choudhury, A. (2022). Computational challenges in hybrid malware detection systems. Journal of Cybersecurity and AI, 11(2), 96-107.
Kumar, S., & Tripathi, V. (2023). Future directions in hybrid AI-graph models for malware propagation detection. AI and Security Journal, 15(1), 178-192.
Madupati, Bhanuprakash. "Integration of Cloud Computing in Smart Cities: Opportunities, Challenges, and Future Direction Paper." Challenges, and Future Direction Paper (December 06, 2019) (2019).
Gupta, Neha, and Vivek Kapoor. "Hybrid cryptographic technique to secure data in web application." Journal of Discrete Mathematical Sciences and Cryptography 23.1 (2020): 125-135.
Talati, Dhruvitkumar V. "Silicon minds: The rise of AI-powered chips." (2021).
Kalluri, Kartheek. "Migrating Legacy System to Pega Rules Process Commander v7. 1." (2015).
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020
Madupati, Bhanuprakash. "Revolution of Cloud Technology in Software Development." Available at SSRN 5146576 (2019).
Gondaliya, Jayraj, et al. "Hybrid security RSA algorithm in application of web service." 2018 1st International Conference on Data Intelligence and Security (ICDIS). IEEE, 2018.
Talati, Dhruvitkumar. "Artificial Intelligence and unintended bias: A call for responsible innovation." (2021).
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
S. Kumari, “AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ”, J. Sci. Tech., vol. 1, no. 1, pp. 809–828, Dec. 2020.
