Adversarial Resilience in Malware Detection: A Two-Stage Structural Analysis Approach for Robust Cybersecurity

Udayakumar, Pavithrasri (2024) Adversarial Resilience in Malware Detection: A Two-Stage Structural Analysis Approach for Robust Cybersecurity. Masters thesis, Dublin, National College of Ireland.

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Abstract

In the fast-changing world of cybersecurity, more and better computer virus attacks are becoming a big issue for keeping digital security safe. This paper suggests a strong two-step system for detecting Malware. It combines simple malware detection and advanced harmful software detection to better identify and fight all types of unwanted programs, even when they use clever hidden methods. Using big computer learning techniques like Logistic Regression, Random Forest and Decision Tree plus the deep thinking skills of Long Short-Term Memory (LSTM), this system shows high results in correctly putting harmful software or normal files. The test, done on a big and complete BODMAS data set, shows the system’s strength against harmful attacks from malware creator. It proves it can change quickly to deal with fast-moving malware or bugs in computer systems. The LSTM model stands out, getting it right 82% of the time. This shows that it is very good at spotting hard to find patterns linked with bad behavior. Even though it’s been successful, the system knows that we need a bigger and more varied set of information to make itself even better. The study helps make computer security stronger by suggesting a new and changeable way to spot malware. It shows the need for constant improvement and growth in order to properly fight off changing threats over time.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Trinh, Anh Duong UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Ciara O'Brien
Date Deposited:	30 May 2025 14:21
Last Modified:	30 May 2025 14:21
URI:	https://norma.ncirl.ie/id/eprint/7717

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