Articles Open Access
AI-Driven Threat Detection and Response Frameworks for Advanced Cybersecurity Applications
Mobile Developer | AI/ML Integration & Edge Intelligence, USA
Abstract
The rapid growth of digital technologies, cloud computing, and interconnected networks has significantly increased cybersecurity threats targeting enterprise systems and critical infrastructures. Traditional cybersecurity mechanisms often struggle to detect sophisticated attacks such as zero-day exploits, advanced persistent threats, ransomware, and insider attacks due to their reliance on static rule-based detection methods. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving threat detection and automated response mechanisms in modern cybersecurity environments. This research explores AI-driven threat detection and response frameworks designed for advanced cybersecurity applications. The study examines the integration of machine learning, deep learning, natural language processing, and behavioral analytics within cybersecurity systems to identify malicious activities, predict emerging threats, and automate incident response procedures. AI-powered frameworks can analyze massive volumes of security data in real time, enabling faster detection of anomalies and minimizing human intervention during security operations. The research further investigates different AI models, detection algorithms, and intelligent response mechanisms used in enterprise cybersecurity infrastructures. Additionally, the study evaluates the advantages, limitations, and implementation challenges associated with AI-driven cybersecurity frameworks. The findings indicate that AI significantly enhances detection accuracy, threat intelligence, response speed, and overall security resilience. However, issues such as false positives, adversarial attacks, data privacy concerns, and high computational requirements remain major challenges requiring continuous research and optimization.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 3 |
| Number | 2 |
| Pages | 3611-3621 |
| Published | 2019-03-05 |
| Issue | Vol. 3 No. 2 (2019): International Research Journal of Innovative Engineering (IRJIE) |
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35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
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38. 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.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. 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.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. 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.
43. 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.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
2. Axelsson, S. (2000). Intrusion detection systems: A survey and taxonomy. Technical Report, Chalmers University.
3. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
4. Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, 13(2), 222–232.
5. Forrest, S., Hofmeyr, S., Somayaji, A., & Longstaff, T. (1996). A sense of self for Unix processes. IEEE Symposium on Security and Privacy, 120–128.
6. Garfinkel, S., & Spafford, G. (2002). Web security, privacy and commerce. O’Reilly Media.
7. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
8. Hu, W., Liao, Y., & Vemuri, V. R. (2003). Robust anomaly detection using support vector machines. International Conference on Machine Learning and Applications, 168–174.
9. Kim, G., Lee, S., & Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690–1700.
10. Lee, W., & Stolfo, S. J. (1998). Data mining approaches for intrusion detection. USENIX Security Symposium, 79–94.
11. Lippmann, R., Haines, J., Fried, D., Korba, J., & Das, K. (2000). Analysis and results of the DARPA offline intrusion detection evaluation. Recent Advances in Intrusion Detection, 162–182.
12. Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
13. Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques. Computer Networks, 51(12), 3448–3470.
14. Ross, T. J. (2010). Fuzzy logic with engineering applications. Wiley.
15. Scarfone, K., & Mell, P. (2007). Guide to intrusion detection and prevention systems. NIST Special Publication 800-94.
16. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy, 305–316.
17. Stallings, W. (2011). Network security essentials: Applications and standards. Pearson.
18. Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 dataset. IEEE Symposium on Computational Intelligence for Security and Defense Applications, 1–6.
19. Wang, W., Sheng, Y., Wang, J., Zeng, X., Ye, X., Huang, Y., & Zhu, M. (2017). HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks. IEEE Access, 6, 1792–1806.
20. Zhou, C., Leckie, C., & Karunasekera, S. (2007). A survey of coordinated attacks and collaborative intrusion detection. Computers & Security, 29(1), 124–140.
21. 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.
22. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. 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.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
26. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
27. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
28. 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.
29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
30. 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.
31. 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.
32. 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.
33. 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.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
38. 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.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. 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.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. 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.
43. 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.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
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