AI and Cybersecurity Research

Led by Dr Mohamad Hazim Bin Md Hanif

SIG Overview

The AI and Cybersecurity SIG stands at the forefront of the battle between artificial intelligence and modern digital threats, dedicated to securing the next generation of intelligent systems. Led by Dr Hazim Hanif, this group bridges the gap between advanced machine learning and comprehensive cyber defence.

Moving beyond traditional reactive measures, the group pioneers research into the dual nature of AI — investigating both offensive AI tactics and intelligent threat detection across diverse environments. With deep expertise in adversarial machine learning and the security implications of Large Language Models (LLMs), the SIG focuses on developing resilient frameworks capable of anticipating, withstanding, and neutralising sophisticated, AI-driven attacks across the digital landscape.

Key Research Areas

  1. 1

    Large Language Models (LLMs) & Deep Representation Learning

    Exploring the capabilities and limitations of transformer-based architectures for security applications, optimising pre-training methodologies, and critically evaluating how data representations impact threat detection.

  2. 2

    Adversarial Machine Learning & Model Robustness

    Securing decentralised AI systems, particularly within federated learning and edge-computing environments, by analysing attack vectors and engineering robust defence mechanisms against adversarial manipulation.

  3. 3

    Intelligent IoT Security & Network Traffic Analysis

    Deploying AI-driven statistical analysis and transformer-based tokenisation to accurately identify IoT devices and classify network traffic across diverse and emerging infrastructures, including 6G networks.

  4. 4

    Automated Threat & Vulnerability Intelligence

    Leveraging advanced machine learning, including hybrid Graph Neural Networks and deep learning ensembles, to autonomously detect structural vulnerabilities and complex threats across various environments.

  5. 5

    AI-Driven Digital Content Integrity

    Utilising supervised boosting models and deep learning frameworks for automated content moderation, detecting opinion spam, regional offensive text, and malicious profiles across digital platforms.

Research Projects

  1. 1

    VulBERTa and the Efficacy of LLMs in Security

    Pioneering simplified source-code pre-training models (VulBERTa) and critically assessing the "Richer Representation Fallacy" to determine whether complex LLM representations add value or noise to automated vulnerability detectors.

  2. 2

    Deep Learning for Open-Source Intelligence and Sentiment Analytics

    Utilising stacked deep-learning algorithms for large-scale data extraction, behavioural tracking, and sentiment analysis on social-media platforms — demonstrating AI's capacity for complex digital intelligence gathering.

  3. 3

    Comprehensive Taxonomies for AI-Assisted Vulnerability Detection

    Systematically mapping the landscape of software vulnerabilities and evaluating the efficacy of various machine-learning approaches to create foundational frameworks and benchmarks for future AI-security tools.

  4. 4

    Intelligent Moderation & Spam Detection Systems

    Building tailored machine-learning pipelines — ranging from supervised boosting for opinion-spam detection to standardised Extra Tree models for regional offensive-text classification (MOTEC) — to preserve the integrity of digital communications.

  5. 5

    Hybrid Graph Neural Networks for Code Security

    Engineering hybrid GNN approaches to mathematically model complex execution paths, applied specifically to detecting sophisticated vulnerabilities in web-based applications and programming languages such as PHP.

Interested in collaborating?

Reach out to discuss postgraduate supervision, joint research, or industry partnerships in AI and cybersecurity.

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