Autonomous Cyber Threat Hunting and Incident Response System Using Artificial Intelligence Techniques
Abstract
The increasing sophistication of cyberattacks has made traditional security systems insufficient for timely detection and response. This paper proposes an Autonomous Cyber Threat Hunting and Incident Response System powered by Artificial Intelligence (AI) techniques. The system leverages machine learning, deep learning, and behavioral analytics to proactively identify anomalies, predict potential threats, and autonomously respond to security incidents in real time. By integrating threat intelligence, automated decision-making, and adaptive learning models, the system reduces human dependency and response latency. The proposed approach enhances cybersecurity resilience, improves accuracy in threat detection, and ensures continuous protection against evolving cyber threats in complex digital environments.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
14681-14687 |
Published |
November 4, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Kristian Nese (%2023). Autonomous Cyber Threat Hunting and Incident Response System Using Artificial Intelligence Techniques. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 6 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14681-14687. https://doi.org/10.15662/ijetemr.2023.0806001 |
References
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34. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
35. 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.
36. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
37. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
38. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
39. 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.
40. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
41. 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.
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. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
44. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034.
2. Sudarsan, V., & Sugumar, R. (2018). Building a Distributed K-Means Model using Simple K-Means of Weka.
3. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
4. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
5. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
6. 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.
7. Boddupally, H. L. (2022). Architectural-driven intelligent refactoring for resilient cloud-native. NET systems. Available at SSRN 6270479.
8. Adepu, R. (2023). Designing FedRAMP-Compliant Cloud Architectures for Secure and Scalable Government Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 10427-10441.
9. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
10. Yamsani, N. (2021). Governance by design: Secure role delegation and approval structures in enterprise master data systems. International Journal of Science, Engineering and Technology, 9(2). https://doi.org/10.5281/zenodo.18296977
11. Katta, T. B. (2022). A Capability Maturity Framework for Event-Driven Integration: Benchmarking Kafka and Pulsar in Enterprise Environments. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9589.
12. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
13. Shewale, V. (2023). AI and Machine Learning for Anomaly Detection in ICS Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(3), 11631.
14. Parasa, M. (2023). Measuring skill graph drift in SAP SuccessFactors Talent Intelligence Hub for career mobility, workforce reskilling, and skills-based talent governance. Advanced International Journal of Multidisciplinary Research, 1(1), 1–27. https://doi.org/10.62127/aijmr.2023.v01i01.1359
15. Subramanyam, S. P. (2023). Cloud infrastructure automation and role-based access governance in Azure Kubernetes services. International Journal of Research Publications in Engineering, Technology and Management, 6(2), 8392–8400.
16. Namdeo, A. (2023). Generative synthetic data pipelines for bias-free BI training. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(1), 10818–10826. https://doi.org/10.15662/IJAESIT.2023.0601003
17. Panyala, V. R. (2022). Engineering event-driven microservices platforms for real-time data processing in cloud ecosystems. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 34–48.
18. Pasumarthi, H. (2023). Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Computer Technology and Electronics Communication, 6(4), 7352–7356
19. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715-3724.
20. Vankayala, S. C. (2023). Governed Autonomy in Reliability Engineering: Integrating Error Budgets with AI-Driven Remediation. J Artif Intell Mach Learn & Data Sci 2023, 1(2), 3191-3196.
21. Adepu, G. (2022). Graph AI–Driven Environmental Intelligence Platforms for Predictive Regulatory Risk Assessment. International Journal of Computer Technology and Electronics Communication, 5(5), 5776-5780.
22. Kanji, R. K. (2022). Generative Query Optimization in Data Warehousing: A Foundation Model-Based Approach for Autonomous SQL Generation and Execution Optimization in Hybrid Architectures. Available at SSRN 5401216.
23. Boddupally, H. L. (2022). Architectural-driven intelligent refactoring for resilient cloud-native. NET systems. Available at SSRN 6270479.
24. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
25. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
26. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
27. 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.
28. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
29. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
30. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
31. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
32. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
33. 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.
34. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
35. 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.
36. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
37. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
38. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
39. 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.
40. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
41. 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.
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. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
44. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034.