Citation

Ali, Atif and Zia, Awais and Razzaque, Abdul and Shahid, Hina and Sheikh, Haroon Tariq and Saleem, Muhammad and Yousaf, Farhan and Muneer, Salman (2024) Enhancing Cybersecurity with Artificial Neural Networks: A Study on Threat Detection and Mitigation Strategies. In: 2024 2nd International Conference on Cyber Resilience (ICCR), 26-28 February 2024, Dubai, United Arab Emirates.

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Abstract

Abstract -This paper reviews the role of artificial neural networks (ANNs) in enhancing cybersecurity measures. It delves into research methodologies employed to detect security breaches, potential threats, and spam, among other cybersecurity challenges. The study further investigates the literature to gauge the efficacy of using AI methodologies in pinpointing system attacks, showcasing the potential of AI in safeguarding various data types, be it academic or industrial, and preventing system vulnerabilities. ANNs, with their predictive capabilities and experiential learning, emerge as advanced security protection and threat detection tools. Additionally, the research delves into developing a hybrid multi-agent system that leverages deep learning for identifying and mitigating cyberattacks, aiming to shed light on the current risks in information systems and propose effective countermeasures.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Cybersecurity, Artificial Intelligence, artificial neural networks, vulnerability
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jul 2024 01:10
Last Modified:	03 Jul 2024 01:10
URII:	http://shdl.mmu.edu.my/id/eprint/12556

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