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Original Article
Nation Guard Antivirus: A Hybrid Multi-Stage Detection System for Nation-State Malware and Advanced Persistent Threats
Nishok S1
Balachandran S2
Thennarasu T3
Keerthana V4
N. Sukanya5
1 2 3 4 Department of Computer Science and Engineering (Cyber Security), United Institute of Technology, Coimbatore, Tamil Nadu, India. 5 Assistant Professor, Department of Computer Science and Engineering, United Institute of Technology, Coimbatore, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 72-80
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502008
References
1. Langner, R. (2011). Stuxnet: Dissecting a Cyberweapon. IEEE Security & Privacy, 9(3), 49-51.
2. Kaspersky Lab Global Research and Analysis Team. (2012). The Flame: Questions and Answers. Kaspersky Lab Technical Report.
3. Symantec Security Response. (2017). WannaCry: Ransomware attacks show strong links to Lazarus Group. Symantec Official Blog.
4. MITRE Corporation. (2023). MITRE ATT&CK Framework: Enterprise Matrix v14. Retrieved from https://attack.mitre.org/
5. Aycock, J. (2006). Computer Viruses and Malware. Advances in Information Security, Vol. 22. Springer.
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of NDSS 2009.
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Symposium 2014.
8. Branco, R. R., Barbosa, G. N., & Neto, P. D. (2012). Scientific but Not Academical Overview of Malware Anti-Debugging, Anti-Disassembly
and Anti-VM Technologies. Black Hat USA 2012.9. Kolbitsch, C., Comparetti, P. M., Kruegel, C., Kirda, E., Zhou, X., & Wang, X. (2009). Effective and Efficient Malware Detection at the End
Host. USENIX Security Symposium 2009.
10. VirusTotal. (2023). VirusTotal API v3 Reference Documentation. Google LLC. Retrieved from https://developers.virustotal.com/reference
11. Saxe, J., & Berlin, K. (2015). Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features.
Proceedings of MALCON 2015.
12. Anderson, H. S., & Roth, P. (2018). EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models. arXiv preprint
arXiv:1804.04637.
13. Moser, A., Kruegel, C., & Kirda, E. (2007). Limits of Static Analysis for Malware Detection. Proceedings of the 23rd ACSAC, 421-430.
14. Roundy, K. A., & Miller, B. P. (2013). Binary-Code Obfuscations in Prevalent Packer Tools. ACM Computing Surveys, 46(1), Article 4.
15. Dinaburg, A., Royal, P., Sharif, M., & Lee, W. (2008). Ether: Malware Analysis via Hardware Virtualization Extensions. Proceedings of
ACM CCS 2008, 51-62.
16. Kaspersky Lab GReAT. (2015). Equation Group: Questions and Answers Version 1.5. Kaspersky Lab Technical Report.
17. Cozzi, E., Graziano, M., Fratantonio, Y., & Balzarotti, D. (2018). Understanding Linux Malware. Proceedings of IEEE Symposium on
Security and Privacy 2018.
18. Chen, X., & Abu Nimeh, S. (2011). Lessons Learned from Cloud-based Evasive Malware. Proceedings of the 2011 eCrime Researchers
Summit.
19. Egele, M., Scholte, T., Kirda, E., & Kruegel, C. (2008). A Survey on Automated Dynamic Malware Analysis Techniques and Tools. ACM
Computing Surveys, 44(2), 1-42.
20. Sikorski, M., & Honig, A. (2012). Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software. No Starch Press.
2. Kaspersky Lab Global Research and Analysis Team. (2012). The Flame: Questions and Answers. Kaspersky Lab Technical Report.
3. Symantec Security Response. (2017). WannaCry: Ransomware attacks show strong links to Lazarus Group. Symantec Official Blog.
4. MITRE Corporation. (2023). MITRE ATT&CK Framework: Enterprise Matrix v14. Retrieved from https://attack.mitre.org/
5. Aycock, J. (2006). Computer Viruses and Malware. Advances in Information Security, Vol. 22. Springer.
6. Bayer, U., Comparetti, P. M., Hlauschek, C., Kruegel, C., & Kirda, E. (2009). Scalable, Behavior-Based Malware Clustering. Proceedings
of NDSS 2009.
7. Kirat, D., Vigna, G., & Kruegel, C. (2014). BareCloud: Bare-metal Analysis-based Evasive Malware Detection. USENIX Security
Symposium 2014.
8. Branco, R. R., Barbosa, G. N., & Neto, P. D. (2012). Scientific but Not Academical Overview of Malware Anti-Debugging, Anti-Disassembly
and Anti-VM Technologies. Black Hat USA 2012.9. Kolbitsch, C., Comparetti, P. M., Kruegel, C., Kirda, E., Zhou, X., & Wang, X. (2009). Effective and Efficient Malware Detection at the End
Host. USENIX Security Symposium 2009.
10. VirusTotal. (2023). VirusTotal API v3 Reference Documentation. Google LLC. Retrieved from https://developers.virustotal.com/reference
11. Saxe, J., & Berlin, K. (2015). Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features.
Proceedings of MALCON 2015.
12. Anderson, H. S., & Roth, P. (2018). EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models. arXiv preprint
arXiv:1804.04637.
13. Moser, A., Kruegel, C., & Kirda, E. (2007). Limits of Static Analysis for Malware Detection. Proceedings of the 23rd ACSAC, 421-430.
14. Roundy, K. A., & Miller, B. P. (2013). Binary-Code Obfuscations in Prevalent Packer Tools. ACM Computing Surveys, 46(1), Article 4.
15. Dinaburg, A., Royal, P., Sharif, M., & Lee, W. (2008). Ether: Malware Analysis via Hardware Virtualization Extensions. Proceedings of
ACM CCS 2008, 51-62.
16. Kaspersky Lab GReAT. (2015). Equation Group: Questions and Answers Version 1.5. Kaspersky Lab Technical Report.
17. Cozzi, E., Graziano, M., Fratantonio, Y., & Balzarotti, D. (2018). Understanding Linux Malware. Proceedings of IEEE Symposium on
Security and Privacy 2018.
18. Chen, X., & Abu Nimeh, S. (2011). Lessons Learned from Cloud-based Evasive Malware. Proceedings of the 2011 eCrime Researchers
Summit.
19. Egele, M., Scholte, T., Kirda, E., & Kruegel, C. (2008). A Survey on Automated Dynamic Malware Analysis Techniques and Tools. ACM
Computing Surveys, 44(2), 1-42.
20. Sikorski, M., & Honig, A. (2012). Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software. No Starch Press.
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