Comprehensive Review of Advanced Machine Learning Techniques for Detecting and Mitigating Zero-Day Exploits

Authors

DOI:

https://doi.org/10.4108/eetsis.6111

Keywords:

Machine Learning, Zero-Day Exploits, Cybersecurity, Threat Detection, Adaptive Algorithms, Deep Learning in Security

Abstract

This paper provides an in-depth examination of the latest machine learning (ML) methodologies applied to the detection and mitigation of zero-day exploits, which represent a critical vulnerability in cybersecurity. We discuss the evolution of machine learning techniques from basic statistical models to sophisticated deep learning frameworks and evaluate their effectiveness in identifying and addressing zero-day threats. The integration of ML with other cybersecurity mechanisms to develop adaptive, robust defense systems is also explored, alongside challenges such as data scarcity, false positives, and the constant arms race against cyber attackers. Special attention is given to innovative strategies that enhance real-time response and prediction capabilities. This review aims to synthesize current trends and anticipate future developments in machine learning technologies to better equip researchers, cybersecurity professionals, and policymakers in their ongoing battle against zero-day exploits.

Author Biography

Hamed Taherdoost, Global University Systems

Hamed Taherdoost is an award-winning leader and R&D professional. He is founder of the Hamta Group | Hamta Business Corporation, Associate Professor and Chair of RSAC at University Canada West, and Director of R&D at Q Minded | Quark Minded Technology Inc. He has over 20 years of experience in both industry and academia sectors. He has worked at international companies from Cyprus, the UK, Malta, Iran, Malaysia, and Canada and has been highly involved in development of several projects in different industries; healthcare, transportation, residential, oil and gas and IT. Apart from industry, he has been a university lecturer in three different parts of the world, Southeast Asia, the Middle East, and North America. Currently, he is an Adjunct Professor at Westcliff University, mentor at Futurpreneur Canada, Advisory Board of Cambridge Scholars Publishing, UK, Senior Technical Consultant at CI Solutions Ltd, and Innotek Consulting Ltd. 

He is a certified cybersecurity technologist and a senior member of IEEE, IAEEEE, IASED, & IEDRC, Fellow Member of ISAC, WGM of IFIP TC11, member of CSIAC, ACT-IAC and AASHE. Hamed has been an active multidisciplinary researcher and R&D specialist involved in several academic and industrial research projects. Currently, he is involved in several multidisciplinary research projects, including studying innovation in information technology, blockchain, and cybersecurity, people’s behavior, and technology acceptance.

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Published

26-06-2024

How to Cite

1.
Mohamed N, Taherdoost H, Madanchian M. Comprehensive Review of Advanced Machine Learning Techniques for Detecting and Mitigating Zero-Day Exploits. EAI Endorsed Scal Inf Syst [Internet]. 2024 Jun. 26 [cited 2024 Nov. 21];11. Available from: https://publications.eai.eu/index.php/sis/article/view/6111

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Section

MTYMEX 2024