Artificial Intelligence Driven Cyber Threat Detection and Prevention Framework for Enterprise Networks

Rohan Das Bengali; Rohan Das Bengali

doi:10.15662/ijetemr.2017.0202001

Artificial Intelligence Driven Cyber Threat Detection and Prevention Framework for Enterprise Networks

Rohan Das Bengali

Dr. B. C. Roy Engineering College, West Bengal, India

Abstract

The increasing dependence of enterprise networks on digital infrastructure has significantly expanded the attack surface for cyber threats. Modern cyberattacks such as ransomware, phishing, Advanced Persistent Threats (APTs), zero-day exploits, and insider attacks have become more sophisticated, adaptive, and difficult to detect using traditional security mechanisms. Conventional rule-based intrusion detection systems and signature-based firewalls are no longer sufficient to handle dynamic and unknown threats in real-time enterprise environments. To address these challenges, Artificial Intelligence (AI)-driven cyber threat detection and prevention frameworks have emerged as a powerful solution. These frameworks leverage machine learning, deep learning, behavioral analytics, and anomaly detection techniques to identify malicious activities, predict potential attacks, and automate response mechanisms. This study proposes an AI-driven cybersecurity framework designed for enterprise networks that integrates real-time monitoring, intelligent threat classification, adaptive learning models, and automated incident response systems. The framework enhances detection accuracy while reducing false positives and response time. It also incorporates predictive analytics to anticipate emerging threats and strengthen network resilience. The research methodology is based on a qualitative and conceptual analysis of secondary data, including academic literature, industry reports, and cybersecurity case studies. Findings suggest that AI-based cybersecurity frameworks significantly improve threat detection efficiency, scalability, and proactive defense capabilities in enterprise environments. The study concludes that AI-driven security systems are essential for modern enterprises to ensure robust, intelligent, and adaptive cyber defense mechanisms.

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 2 No. 2 (2017): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2017.0202001
Pages	1387-1394
Published	March 8, 2017
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Rohan Das Bengali (%2017). Artificial Intelligence Driven Cyber Threat Detection and Prevention Framework for Enterprise Networks. International Journal of Emerging Trends in Engineering and Management Research , Vol. 2 No. 2 (2017): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 1387-1394. https://doi.org/10.15662/ijetemr.2017.0202001

References

1. Christidis, K., & Devetsikiotis, M. (2016). Blockchains and smart contracts for the internet of things. IEEE Access, 4, 2292–2303.
2. Dorri, A., Kanhere, S. S., & Jurdak, R. (2016). Blockchain in internet of things: Challenges and solutions. arXiv preprint arXiv:1608.05187.
3. Liang, X., Shetty, S., Tosh, D., Kamhoua, C. A., Kwiat, K. A., & Njilla, L. (2017). ProvChain: A blockchain-based data provenance architecture in cloud environment. IEEE International Conference on Cloud Computing.
4. Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system.
5. Park, J. H., & Park, J. H. (2017). Blockchain security in cloud computing: Use cases, challenges, and solutions. Symmetry, 9(8), 164.
6. Sharma, P., et al. (2016). Secure data sharing in cloud computing using cryptographic techniques. International Journal of Computer Applications.
7. Xia, Q., Sifah, E. B., Asamoah, K. O., Gao, J., Du, X., & Guizani, M. (2017). MeDShare: Trust-less medical data sharing among cloud service providers via blockchain. IEEE Access, 5, 14757–14767.
8. Zhang, Y., & Wen, J. (2017). The IoT electric business model: Using blockchain technology for the internet of things. Peer-to-Peer Networking and Applications, 10(4), 983–994.
9. Zyskind, G., Nathan, O., & Pentland, A. (2015). Decentralizing privacy: Using blockchain to protect personal data. IEEE Security and Privacy Workshops.
10. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
11. Mathew, A., & Asari, V. K. (2012, August). Local histogram based descriptor for tracking in wide area imagery. In International Conference on Information Processing (pp. 119-128). Berlin, Heidelberg: Springer Berlin Heidelberg.
12. Murugeshwari, B., Sarukesi, K., & Jayakumar, C. (2010, March). An efficient method for knowledge hiding through database extension. In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (pp. 342-344). IEEE.
13. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
14. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
15. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
16. Kumar, J. (2013). Preservation of the Privacy for Multiple Custodian Systems with Rule Sharing. Journal of Computer Science.
17. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
18. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
19. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
20. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
21. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
22. Jagadeesh, S., & Soundappan, R. S. (2014). Survey on knowledge discovery in speech emotion detection. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4476-4481.
23. Murugeshwari, B., & Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
24. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
25. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
26. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
27. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta, 2(1), 21-27.
28. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
29. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
30. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
31. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.