Published July 30, 2022 | Version CC BY-NC-ND 4.0
Journal article Open

Impact and Feasibility of harnessing AI and ML in the realm of Cybersecurity to detect Network Intrusions: A Review

Creators

  • 1. Security Network Consulting Engineer, Aryaka Networks, Bengaluru (Karnataka), India.
  • 2. Associate Professor, Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru (Karnataka), India.

Contributors

Contact person:

  • 1. Security Network Consulting Engineer, Aryaka Networks, Bengaluru (Karnataka), India.

Description

Abstract: Remarkable advances in cyberspace, have amassed a magnanimous set of Internet users worldwide. While people engage in various activities and use the web for various needs, the prospective fear of cyber attacks, crime and threats is indubitable. Though a plethora of preventive measures are in use, it is impossible to circumvent cyber threats completely. Cybersecurity is a domain that deals with prevention of cyber attacks by use of effective precautionary and remedial measures. With the advent of Artificial Intelligence (AI) and Machine Learning (ML) and its profound scope in contemporary technical innovations, it is a critical necessity to inculcate its techniques in enhancement of existing cybersecurity techniques. This paper offers a detailed review of the concepts of cybersecurity, commonly encountered cyber attacks, the relevance of AI and ML in cybersecurity along with a comparative performance analysis of distinct ML algorithms to combat network anomaly detection and network intrusion detection.

Notes

Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

Files

B71500711222.pdf

Files (424.0 kB)

Name Size Download all
md5:b83a612389701c5b4cb46e3b4fc7474d
424.0 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2277-3878 (ISSN)

References

  • Craigen, D., Diakun-Thibault, N., & Purse, R. 2014. Defining Cybersecurity. Technology Innovation Management Review, 4(10): 13-21. http://doi.org/10.22215/timreview/835
  • R. A. Kemmerer, "Cybersecurity," 25th International Conference on Software Engineering, 2003. Proceedings., 2003, pp. 705-715, doi: 10.1109/ICSE.2003.1201257.
  • Jang-Jaccard, Julian, and Surya Nepal. "A survey of emerging threats in cybersecurity." Journal of Computer and System Sciences 80.5 (2014): 973-993.
  • Symantec Security Summary 2020. [online] https://symantec-enterprise-blogs.security.com/blogs/feature-stories/sy mantec-security-summary-june-2020 (Accessed 21st September 2021)
  • Cyber Security Report 2020 [online]https://www.ntsc.org/assets/pdfs/cyber-security-report-2020.pd f (Accessed 25th September 2021)
  • Bendovschi, Andreea. "Cyber-attacks–trends, patterns and security countermeasures." Procedia Economics and Finance 28 (2015): 24-31.
  • Biju, Jibi Mariam, Neethu Gopal, and Anju J. Prakash. "Cyber attacks and its different types." International Research Journal of Engineering and Technology 6.3 (2019): 4849-4852.
  • Fischer, Eric A. "Cybersecurity issues and challenges: In brief." (2014).
  • Hussain, Abdulla, Azlinah Mohamed, and Suriyati Razali. "A Review on Cybersecurity: Challenges & Emerging Threats." Proceedings of the 3rd International Conference on Networking, Information Systems & Security. 2020.
  • Pogrebna, Ganna, and Mark Skilton. "Cybersecurity Threats: Past and Present." Navigating New Cyber Risks. Palgrave Macmillan, Cham, 2019. 13-29.
  • Geluvaraj, B., P. M. Satwik, and TA Ashok Kumar. "The future of cybersecurity: Major role of artificial intelligence, machine learning, and deep learning in cyberspace." International Conference on Computer Networks and Communication Technologies. Springer, Singapore, 2019.
  • Bresniker, Kirk, et al. "Grand challenge: Applying artificial intelligence and machine learning to cybersecurity." Computer 52.12 (2019): 45-52.
  • Li, Jian-hua. "Cyber security meets artificial intelligence: a survey." Frontiers of Information Technology & Electronic Engineering 19.12 (2018): 1462-1474.
  • Sarker, Iqbal H., Md Hasan Furhad, and Raza Nowrozy. "Ai-driven cybersecurity: an overview, security intelligence modeling and research directions." SN Computer Science 2.3 (2021): 1-18.
  • Soni, Vishal Dineshkumar. "Challenges and Solution for Artificial Intelligence in Cybersecurity of the USA." Available at SSRN 3624487 (2020).
  • Yuan, Yali & Kaklamanos, Georgios & Hogrefe, Dieter. (2016). A Novel Semi-Supervised Adaboost Technique for Network Anomaly Detection. 111-114. 10.1145/2988287.2989177.
  • Xueqin Zhang, Chunhua Gu and Jiajun Lin, "Support Vector Machines for Anomaly Detection," 2006 6th World Congress on Intelligent Control and Automation, 2006, pp. 2594-2598, doi: 10.1109/WCICA.2006.1712831.
  • C. Warrender, S. Forrest and B. Pearlmutter. "Detecting Intrusions Using System Calls: Alternative Data Models." In Proceedings of 1999 IEEE Symposium on Security and Privacy, pp 133-145, Oakland, 1999.
  • Hu, Wenjie & Liao, Yihua & Vemuri, Rao. (2003). Robust Anomaly Detection Using Support Vector Machines. Proceedings of the International Conference on Machine Learning.
  • Bama, S. Sathya, Irfan Uddin Ahmed and Hindusthan. "Network Intrusion Detection using Clustering: A Data Mining Approach." International Journal of Computer Applications 30 (2011): 14-17.
  • S., Shilpashree. (2019). Decision Tree: A Machine Learning for Intrusion Detection. International Journal of Innovative Technology and Exploring Engineering. 8. 5. 10.35940/ijitee.F1234.0486S419.
  • Li, Wei. (2004). Using genetic algorithm for network intrusion detection.
  • S. E. Benaicha, L. Saoudi, S. E. B. Guermeche and O. Lounis, "Intrusion detection system using genetic algorithm," 2014 Science and Information Conference, 2014, pp. 564-568, doi: 10.1109/SAI.2014.6918242.
  • Yin, Qingbo & Shen, Li-Ran & Zhang, Ru-Bo & Li, Xue-Yao & Wang, Hui-Qiang. (2003). Intrusion detection based on hidden Markov model. 10.1109/ICMLC.2003.1260114.
  • Panda, Mrutyunjaya & Patra, Manas. (2007). Network intrusion detection using naive bayes. 7.
  • Liao, Yihua & Vemuri, Rao. (2002). Use of K-Nearest Neighbor classifier for intrusion detection. Computers & Security. 21. 439-448. 10.1016/S0167-4048(02)00514-X.
  • Zhang, Jiong & Zulkernine, Mohammad & Haque, A.. (2008). Random-Forests-Based Network Intrusion Detection Systems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on. 38. 649 - 659. 10.1109/TSMCC.2008.923876.
  • K. Sethi, R. Kumar, N. Prajapati and P. Bera, "Deep Reinforcement Learning based Intrusion Detection System for Cloud Infrastructure," 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), 2020, pp. 1-6, doi: 10.1109/COMSNETS48256.2020.9027452.
  • Mulay, Snehal & Devale, P.R. & Garje, Goraksh. (2010). Intrusion Detection System Using Support Vector
  • Machine and Decision Tree. International Journal of Computer Applications. 3. 10.5120/758-993.
  • S. Mukkamala, G. I. Janoski, and A. H. Sung. "Intrusion Detection Using Support Vector Machines", Proceedings of the High Performance Computing Symposium - HPC 2002, pp 178-183, San Diego, April 2002.
  • Kotpalliwar, Manjiri & Wajgi, Rakhi. (2015). Classification of Attacks Using Support Vector Machine (SVM) on KDDCUP'99 IDS Database. 987-990. 10.1109/CSNT.2015.185.

Subjects

ISSN: 2277-3878 (Online)
https://portal.issn.org/resource/ISSN/2277-3878
Retrieval Number: 100.1/ijrte.B71500711222
https://www.ijrte.org/portfolio-item/b71500711222/
Journal Website: www.ijrte.org
https://www.ijrte.org/
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org/