Ensuring Network Security in Remote Work Environment

Mohammad, Mustafa Maveeya Maaz (2024) Ensuring Network Security in Remote Work Environment. Masters thesis, Dublin, National College of Ireland.

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Abstract

The outbreak of the COVID-19 pandemic has led to the increased use of remote work, and this has greatly improved the uncertainties of cybersecurity hence the need for sophisticated IDS for remote systems. In this work, a comparative study is presented between IDS ML and DL models by using the CICIDS-2017 dataset. The analysis compares clustering algorithms – K-Means, DBSCAN, and DL models – LSTM, Attention LSTM, and Transformer – in threat identification, including zero-day threats. Among the models, the highest precision (95.78%) is seen for the Transformer, as well as a relatively high F1-score with a value of 93.76% This ability of the Transformer model is connected to the model’s ability to work with dependencies between features. LSTM and Attention LSTM models both provided good rates of recall at 98.19% and 98.15% respectively, which is especially good for identifying frequently occurring, known attack types. In this task, supervised methods outperformed unsupervised methods like KMeans and DBSCAN with 15.25% and 0.76% accuracy, respectively, as both algorithms are weak when it comes to a high number of attributes data. This discussion also highlights the use of DL model’s effectiveness in structured IDS application while paving ways to consider the future research with transformer-based algorithms for zero-day attacks.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mahajan, Kamil 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 R Medicine > Diseases > Outbreaks of disease > Epidemics > COVID-19 Pandemic, 2020- H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work 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:	23 Jul 2025 15:22
Last Modified:	23 Jul 2025 15:22
URI:	https://norma.ncirl.ie/id/eprint/8227

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