Zero-Day Attack Detection using Ensemble Technique

##plugins.themes.academic_pro.article.main##

Fawaz Wangde
Shivam Mulay
Rahul Adhao
Vinod Pachghare

Abstract

The zero-day attacks exploit the new vulnerabilities in the system or old vulnerabilities in a new way. Zero-day
attacks are sustainable in the system exploiting the system until detected or until the patch is released, this
creates a dire need to detect zero-day attacks in the system. The model in this proposed paper is an outlier-based
model trained using benign and known attack traffic to detect traffic of unknown attacks. The proposed system
successfully detected most of the unknown attack traffic, achieving higher results in Web, Infiltration, SSH, FTP,
and Botnet attacks.

##plugins.themes.academic_pro.article.details##

How to Cite
Wangde, F., Mulay, S., Adhao, R., & Pachghare, V. (2021). Zero-Day Attack Detection using Ensemble Technique. International Journal of Next-Generation Computing, 12(5). https://doi.org/10.47164/ijngc.v12i5.423

References

  1. Abri, F., Siami-Namini, S., Khanghah, M. A., Soltani, F. M., and Namin, A. S. 2019. Can machine/deep learning classi ers detect zero-day malware with high accuracy? In 2019 IEEE International Conference on Big Data (Big Data). 3252-3259. DOI: https://doi.org/10.1109/BigData47090.2019.9006514
  2. Al-Rushdan, H., Shurman, M., Alnabelsi, S. H., and Althebyan, Q. 2019. Zero-day attack detection and prevention in software-de fined networks. In 2019 International Arab Conference on Information Technology (ACIT). 278-282. DOI: https://doi.org/10.1109/ACIT47987.2019.8991124
  3. Aleroud, A. and Karabatis, G. 2013. Toward zero-day attack identi cation using linear data transformation techniques. In 2013 IEEE 7th International Conference on Software Security and Reliability. 159-168. DOI: https://doi.org/10.1109/SERE.2013.16
  4. Bilge, L. and Dumitras, T. 2012. Before we knew it: An empirical study of zero-day attacks in the real world. Association for Computing Machinery, New York, NY, USA. DOI: https://doi.org/10.1145/2382196.2382284
  5. He, Z., Miari, T., Makrani, H. M., Aliasgari, M., Homayoun, H., and Sayadi, H. 2021. When machine learning meets hardware cybersecurity: Delving into accurate zero-day malware detection. In 2021 22nd International Symposium on Quality Electronic Design (ISQED). 85-90. DOI: https://doi.org/10.1109/ISQED51717.2021.9424330
  6. Hindy, H., Atkinson, R., Tachtatzis, C., Colin, J.-N., Bayne, E., and Bellekens, X. 2020. Utilising deep learning techniques for effective zero-day attack detection. Electronics 9, 10. DOI: https://doi.org/10.3390/electronics9101684
  7. Holm, H. 2014. Signature based intrusion detection for zero-day attacks: (not) a closed chapter? In 2014 47th Hawaii International Conference on System Sciences. 4895-4904. DOI: https://doi.org/10.1109/HICSS.2014.600
  8. Innab, N., Alomairy, E., and Alsheddi, L. 2018. Hybrid system between anomaly based detection system and honeypot to detect zero day attack. In 2018 21st Saudi Computer Society National Computer Conference (NCC). 1-5. DOI: https://doi.org/10.1109/NCG.2018.8593030
  9. Kumar, V. and Sinha, D. 2021. A robust intelligent zero-day cyber-attack detection technique. Complex & Intelligent Systems. DOI: https://doi.org/10.1007/s40747-021-00396-9
  10. Kyatham, A. S., Nichal, M. A., and Deore, B. S. 2020. A novel approach for network intrusion detection using probability parameter to ensemble machine learning models. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). 608-613. DOI: https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000113
  11. Mirza, A. H. 2018. Computer network intrusion detection using various classi ers and ensemble learning. In 2018 26th Signal Processing and Communications Applications Conference (SIU). 1-4. DOI: https://doi.org/10.1109/SIU.2018.8404704
  12. Nandi, S., Maity, S., and Das, M. 2020. Nidf: An ensemble-inspired feature learning frame-work for network intrusion detection. In 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE). 9-12. DOI: https://doi.org/10.1109/WIECON-ECE52138.2020.9397993
  13. Sejr, J. H., Zimek, A., and Schneider-Kamp, P. 2020. Explainable detection of zero day web attacks. In 2020 3rd International Conference on Data Intelligence and Security (ICDIS). 71-78. DOI: https://doi.org/10.1109/ICDIS50059.2020.00016
  14. Vishwakarma, R. and Jain, A. K. 2019. A honeypot with machine learning based detection framework for defending iot based botnet ddos attacks. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). 1019-1024. DOI: https://doi.org/10.1109/ICOEI.2019.8862720
  15. Zoppi, T., Ceccarelli, A., and Bondavalli, A. 2021. Unsupervised algorithms to detect zero-day attacks: Strategy and application. IEEE Access 9, 90603-90615. DOI: https://doi.org/10.1109/ACCESS.2021.3090957