MSc in Cyber Security

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2918

Students enrolled in the MSc in Cyber Security programme are required to submit a thesis as a compulsory component of their degree requirements. This collection comprises merit-based theses submitted by postgraduate candidates specialising in Cyber Security. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.
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Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Author: search.filters.author.Aththanayaka A.M.R.E.Subject: search.filters.subject.MNIST

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    PublicationOpen Access
    Evaluating and Enhancing the Robustness of CNN algorithm Against Adversarial Attacks: A Case Study on MNIST
    (Sri Lanka Institute of Information Technology, 2025-12) Aththanayaka A.M.R.E.
    The Convolutional Neural Networks (CNNs) have achieved exceptional performance in computer vision tasks, particularly in image classification domains such as MNIST digit recognition. However, their susceptibility to adversarial attacks poses serious security threats that limit their deployment in real-world applications. This research examines CNNs vulnerability through systematic evaluation of five potent adversarial attacks such as FGSM, BIM, PGD, Deep Fool, and Carlini-Wagner on MNIST dataset. The baseline CNN model achieves 99.23% accuracy on clean data, However, adversarial attacks which subtly perturbed inputs designed to fool classifiers cause catastrophic performance degradation, reducing accuracy to as low as 8.91%. To address these vulnerabilities, this study proposes CADF: a Comprehensive Cyber Attack Detection Framework which implements a multi-layered defense strategy. The framework incorporates a binary detection classifier achieving 99.56% accuracy in identifying adversarial examples, followed by a multi-class attack identifier with 93.56% accuracy in categorizing specific threat types. CADF's adaptive defense engine dynamically selects optimal countermeasures including feature squeezing, spatial smoothing, and ensemble defenses based on the identified attack characteristics. Experimental results demonstrate that CADF restores model accuracy under multi-attack scenarios while maintaining high performance on clean samples and achieving real-time processing capabilities. This integrated approach provides a scalable and efficient solution for enhancing CNN robustness without compromising computational performance, offering significant advancements in securing deep learning systems against evolving adversarial threats.