الفهرس 
Network SecurityOnly 14 pages are availabe for public view
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Abstract
In today’s digitally-transformed world, the Internet and information networks play a
vital role in virtually every aspect of people’s lives, and in various sectors, including education,
healthcare, finance, and defence. However, this digital revolution has also brought about
significant challenges, particularly concerning the security and integrity of these networks.
With the increasing reliance on Internet-based services and the exponential growth of data
volumes, malicious actors have emerged, seeking to exploit vulnerabilities and manipulate
sensitive information for personal gain or malicious intent. The consequences of successful
cyber-attacks can be devastating, leading to financial loss, privacy breaches, disruption of
critical services, and even threats to national security. To ensure the continuous availability,
confidentiality, and integrity of data in this interconnected landscape, safeguarding the
networks against cyber threats has become an absolute necessity. Among the essential
components of network security, Intrusion Detection Systems (IDSs) play a pivotal role in
detecting and mitigating potential threats. IDSs monitor network traffic in real-time, analysing
patterns and behaviours to identify anomalies that may indicate unauthorized access attempts,
malicious activities, or suspicious behaviour. By promptly alerting network administrators to
potential threats. IDSs enable timely response and mitigation measures, minimizing the impact
of cyber-attacks and ensuring the continuous operation of critical systems. However, the everevolving nature of cyber threats necessitates the development of intelligent and adaptive
defence mechanisms. Traditional IDSs often struggle to keep pace with the rapidly changing
landscape of attack techniques, making it imperative for network security researchers to
explore innovative approaches that can enhance the effectiveness of IDSs. This thesis presents
three automated anomaly detection models using Convolutional Neural Networks (CNNs) to
augment the capabilities of IDSs. The first model is trained from scratch using the NSL-KDD
intrusion detection dataset, while the other two models are built based on pre-trained models,
namely the Visual Geometry group method using 19 layers (VGG19) and the Residual
Network using 152 layers (ResNet152), with the UNSW-NB15 database serving as the
evaluation dataset. By leveraging the power of Deep Learning (DL) methodology, the proposed
models demonstrate remarkable accuracy in detecting network intrusions concurrently with
minimizing the false predictions. Through extensive experimentation and comprehensive
comparisons with state-of-the-art IDS models, the effectiveness and superiority of the proposed
models are substantiated. Ultimately, this research contributes to the ongoing efforts of
securing Internet and information networks in the age of digital transformation.