Adaptive Machine Learning for Real-Time Intrusion Detection in Networks

Sobha, Anju (2025) Adaptive Machine Learning for Real-Time Intrusion Detection in Networks. Masters thesis, Dublin, National College of Ireland.

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Abstract

With the complexity of the cyber threats, the issue of developing more intelligent and flexible systems that can detect the known and unknown intrusions in real-time has become a burning matter. The paper suggests a hybrid threat detection scheme that combines the different data sampling techniques and imbalanced data treatment with the classical machine learning (ML) models in such a way that IDS performance becomes more efficient and precise to make a generic space optimised and self-learning cyber security module. Classic ML models like a Random Forest, support Vegetable Machines (SVM), and Neural Network have been extensively applied in the intrusion detection because they have the capacity to learn a pattern based on the past network traffic data. Nevertheless, they are constrained in scaling to high-dimensional data, identifying high-dimensional data or in recognizing zero-day attacks. This work implements test cybersecurity data, such as NSL-KDD etc., to gain an insight into the performance comparison of classical and quantum-enhanced models. Normalization and dimensionality reduction techniques of data preprocessing are used to provide the best possible features representation of classical and quantum classifiers. The findings reveal that classical models are performing well with the traditional forms of attacks, to identify the slightest anomalies and attack vectors complicated in nature with better accuracy and reduced false-positive rates in predetermined cases.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Prior, Michael UNSPECIFIED
Uncontrolled Keywords:	Classical Machine Learning models; Data Imbalance techniques; Feature extraction; Threat Detection; Hybrid Model; Anomaly Detection; Network Security
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks 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 Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	17 Jun 2026 09:22
Last Modified:	17 Jun 2026 09:22
URI:	https://norma.ncirl.ie/id/eprint/9379

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