Impact and Feasibility of harnessing AI and ML in the realm of Cybersecurity to detect Network Intrusions: A Review
Swathi Dayanand1, Chaitra Nagaraj2
1Swathi Dayanand, Security Network Consulting Engineer, Aryaka Networks, Bengaluru (Karnataka), India.
2Dr. Chaitra N, Associate Professor, Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru (Karnataka), India.
Manuscript received on 26 June 2022 | Revised Manuscript received on 01 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 96-102 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B71500711222 | DOI: 10.35940/ijrte.B7150.0711222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Cyber Security, Machine Learning, Network Anomaly Detection, Network Intrusion Detection
Scope of the Article: Machine Learning