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Original Article
Zero Guardian-XDR: An Intelligent Lightweight Framework for Real-Time Threat Detection, Vulnerability Assessment and Automated Security Response
Sanjay Maheswaran1
Shivanisree E K2
Rupavathi P3
Ramya D4
Dr. H. Abdul Rauf5
1 2 3 4 Department of Computer Science and Engineering (Cyber Security), United Institute of Technology, Coimbatore, Tamil Nadu, India 5 Principal, United Institute of Technology, Coimbatore, Tamil Nadu, India
Published Online: May-August 2026
Pages: 13-19
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502002
References
1. M. Roesch, "Snort: Lightweight intrusion detection for networks," in Proc. USENIX LISA, 1999, pp. 229-238.
2. M. Tavallaee et al., "A detailed analysis of the KDD Cup 99 dataset," in Proc. IEEE CISDA, 2009.
3. N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection," in Proc. MilCIS, 2015.
4. I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset," in Proc. ICISSP, 2018, pp. 108-116.
5. R. Lippmann et al., "Evaluating intrusion detection systems: The 1998 DARPA evaluation," in Proc. DARPA, 2000.
6. A. L. Buczak and E. Guven, "A survey of data mining and machine learning methods for cyber security intrusion detection," IEEE Commun. Surveys Tuts., vol. 18, no. 2, pp. 1153-1176, 2016.
7. R. Sommer and V. Paxson, "Outside the closed world: On using machine learning for network intrusion detection," in Proc. IEEE S&P, 2010.
8. G. Kim, S. Lee, and S. Kim, "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection," Expert Syst. Appl., vol. 41, no. 4, pp. 1690-1700, 2014.
9. A. Javaid et al., "A deep learning approach for network intrusion detection system," in Proc. EAI SecureComm, 2016.
10. C. Yin et al., "A deep learning approach for intrusion detection using recurrent neural networks," IEEE Access, vol. 5, pp. 21954-21961, 2017.
11. M. Sakurada and T. Yairi, "Anomaly detection using autoencoders with nonlinear dimensionality reduction," in Proc. MLSDA, 2014.
12. D. Kwon et al., "A survey of deep learning-based network anomaly detection," Cluster Comput., vol. 22, pp. 949-961, 2019.
13. Y. Mirsky et al., "Kitsune: An ensemble of autoencoders for online network intrusion detection," in Proc. NDSS, 2018.
14. N. Shone et al., "A deep learning approach to network intrusion detection," IEEE Trans. Emerg. Topics Comput. Intell., vol. 2, no. 1, pp. 41-50, 2018.
15. M. A. Ferrag et al., "Deep learning for cyber security intrusion detection," J. Inf. Secur. Appl., vol. 50, 2020.
16. S. Aljawarneh et al., "Anomaly-based intrusion detection system through feature selection analysis," J. Comput. Sci., 2018.
17. MITRE Corporation, "MITRE ATT&CK Enterprise Framework," https://attack.mitre.org, 2023.
18. K. Scarfone and P. Mell, "Guide to intrusion detection and prevention systems (IDPS)," NIST SP 800-94, 2007.
19. W. Stallings, Network Security Essentials: Applications and Standards, 6th ed. Pearson, 2017.
20. AlienVault, "Open Threat Exchange (OTX)," https://otx.alienvault.com, 2024.
21. R. Vinayakumar et al., "Deep learning approach for intelligent intrusion detection system," IEEE Access, vol. 7, pp. 41525-41550, 2019.
22. Z. Ahmad et al., "Network intrusion detection system: A systematic study," Trans. Emerg. Telecommun. Technol., 2021.
23. I. H. Sarker, "Machine learning for intelligent data analysis in cybersecurity," Ann. Data Sci., 2021.
24. A. Khraisat et al., "Survey of intrusion detection systems: Techniques, datasets and challenges," Cybersecurity, vol. 2, p. 20, 2019.
25. M. Alazab et al., "Zero-day malware detection based on supervised learning algorithms," IEEE Access, 2020.
2. M. Tavallaee et al., "A detailed analysis of the KDD Cup 99 dataset," in Proc. IEEE CISDA, 2009.
3. N. Moustafa and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection," in Proc. MilCIS, 2015.
4. I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward generating a new intrusion detection dataset," in Proc. ICISSP, 2018, pp. 108-116.
5. R. Lippmann et al., "Evaluating intrusion detection systems: The 1998 DARPA evaluation," in Proc. DARPA, 2000.
6. A. L. Buczak and E. Guven, "A survey of data mining and machine learning methods for cyber security intrusion detection," IEEE Commun. Surveys Tuts., vol. 18, no. 2, pp. 1153-1176, 2016.
7. R. Sommer and V. Paxson, "Outside the closed world: On using machine learning for network intrusion detection," in Proc. IEEE S&P, 2010.
8. G. Kim, S. Lee, and S. Kim, "A novel hybrid intrusion detection method integrating anomaly detection with misuse detection," Expert Syst. Appl., vol. 41, no. 4, pp. 1690-1700, 2014.
9. A. Javaid et al., "A deep learning approach for network intrusion detection system," in Proc. EAI SecureComm, 2016.
10. C. Yin et al., "A deep learning approach for intrusion detection using recurrent neural networks," IEEE Access, vol. 5, pp. 21954-21961, 2017.
11. M. Sakurada and T. Yairi, "Anomaly detection using autoencoders with nonlinear dimensionality reduction," in Proc. MLSDA, 2014.
12. D. Kwon et al., "A survey of deep learning-based network anomaly detection," Cluster Comput., vol. 22, pp. 949-961, 2019.
13. Y. Mirsky et al., "Kitsune: An ensemble of autoencoders for online network intrusion detection," in Proc. NDSS, 2018.
14. N. Shone et al., "A deep learning approach to network intrusion detection," IEEE Trans. Emerg. Topics Comput. Intell., vol. 2, no. 1, pp. 41-50, 2018.
15. M. A. Ferrag et al., "Deep learning for cyber security intrusion detection," J. Inf. Secur. Appl., vol. 50, 2020.
16. S. Aljawarneh et al., "Anomaly-based intrusion detection system through feature selection analysis," J. Comput. Sci., 2018.
17. MITRE Corporation, "MITRE ATT&CK Enterprise Framework," https://attack.mitre.org, 2023.
18. K. Scarfone and P. Mell, "Guide to intrusion detection and prevention systems (IDPS)," NIST SP 800-94, 2007.
19. W. Stallings, Network Security Essentials: Applications and Standards, 6th ed. Pearson, 2017.
20. AlienVault, "Open Threat Exchange (OTX)," https://otx.alienvault.com, 2024.
21. R. Vinayakumar et al., "Deep learning approach for intelligent intrusion detection system," IEEE Access, vol. 7, pp. 41525-41550, 2019.
22. Z. Ahmad et al., "Network intrusion detection system: A systematic study," Trans. Emerg. Telecommun. Technol., 2021.
23. I. H. Sarker, "Machine learning for intelligent data analysis in cybersecurity," Ann. Data Sci., 2021.
24. A. Khraisat et al., "Survey of intrusion detection systems: Techniques, datasets and challenges," Cybersecurity, vol. 2, p. 20, 2019.
25. M. Alazab et al., "Zero-day malware detection based on supervised learning algorithms," IEEE Access, 2020.
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