Assessment of existing cyber-attack detection models for web-based systems
Jaramogi Oginga Odinga, University of Science and Technology, Kenya.
Review Article
Global Journal of Engineering and Technology Advances, 2023, 15(01), 070–089.
Article DOI: 10.30574/gjeta.2023.15.1.0075
Publication history:
Received on 06March 2023; revised on 19 April 2023; accepted on 21 April 2023
Abstract:
In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.
Keywords:
Cyber-Attack; Web-Based Systems; Detection Models; Machine Learning; Deep Learning
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0