Citation

Iftikhar, Umna and Al-Helali, Marwah Zaid Mohammed and Attaullah, Hafiz Muhammad and Butt, Ramzan Ali and Ahmed Ibrahim, Thabit Mahmood Thabit (2026) An Adaptive Model for Unmasking Zero-Day Threats Using Federated Learning. In: Proceedings of the 3rd International Conference on Emerging Trends and Innovation (3rd ICETI). Springer Nature, pp. 13-34. ISBN 978-3-032-22425-5, 978-3-032-22426-2

Text An Adaptive Model for Unmasking Zero-Day Threats Using Federated Learning _ Springer Nature Link.pdf - Published Version Restricted to Repository staff only Download (729kB)

Abstract

Computer systems are getting better and better at processing data with its architecture becoming much more complex. This makes them vulnerable to attacks that can steal sensitive information and breach users’ privacy. Some of the ways that this can happen are called “zero day attacks.” These are attacks that no-one knows and are existing under the hood. Only skilled hackers are able to identify and exploit it in applications, systems, IoT networks etc. which has been seen in the past. Since data breaches have increased over the last couple of years the security firms have started to take measures for mitigation of zero-day attacks. Researchers are now using different techniques such as Deep Learning (Outlier and Transductive), High Performance Soft Computing (HPSC), advancing the signature based detection mechanisms of antivirus software and much more. Unfortunately, the zero-day attack vector is much more complicated so there is no permanent resolution to this issue. Every solution that has been proposed up till now has some drawbacks as well. This paper performs a detailed as well as comparative analysis of different methods that are being used for zero-day attack detection.

Item Type:	Book Section
Uncontrolled Keywords:	Cybersecurity, deep learning detection
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	01 Oct 2026 03:46
Last Modified:	01 Oct 2026 03:46
URII:	http://shdl.mmu.edu.my/id/eprint/16770

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