Our research on Secure Connected Devices, led by Professor Máire O’Neill, spans multiple areas of hardware and cryptographic security, addressing both current and emerging challenges. Areas of key focus include Trusted Hardware, with cutting-edge work on PUF-based authentication, hardware Trojan detection, side-channel analysis, and the intersection of security with approximate computing and deep learning.  

Our work in Advanced Cryptographic Architectures explores post-quantum designs, hybrid quantum/PQC approaches, homomorphic and attribute-based encryption, and password authenticated key exchange. Together, these research streams contribute to building robust, future-ready systems resilient against evolving cyber threats.

The Networked Systems and ICS Security Group, led by Dr Kieran McLaughlin, develops advanced security solutions for highly interconnected digital infrastructures. Research areas include high-performance network processing for cybersecurity in datacentres and DPUs, AI-driven threat detection, malware and IoT security, and performance-optimised architectures for emerging networks such as O-RAN and SAGINs.

The group also advances security and resilience in Industrial Control Systems, smart grids, SCADA, and IT-OT networks, with expertise in Digital Twins, PLC security, and reinforcement learning for autonomous threat detection and response.

Dr. Jesús Martínez del Rincón leads the Security Intelligence Group at CSIT, advancing next-generation security that is both transparent to users and ensures high service availability. Moving beyond traditional preventative controls, the group focuses on detection, accountability, and situational awareness, driven by cutting-edge AI-powered security analytics. Their research develops online unsupervised learning and reasoning techniques that integrate experiential knowledge with detected events to deliver high-level situational awareness.

Core expertise spans probabilistic modelling, deep learning, graph mining, and evidential reasoning, applied to diverse data sources such as software op-codes, network traffic, security alerts, video, and access logs. Applications include malware and intrusion detection, video surveillance, and advanced persistent threat analysis.

• Cybersecurity engineering: bridging the security gaps in advanced automotive systems and ISO/SAE 21434
• Understanding the security implications in O-RAN with abusive adversaries
• Optimising vulnerability triage in DAST with deep learning
• A framework for enhancing cyber incident response with security-enhancing digital twins in cyber–physical systems
• A computer vision approach to monitor activity in commercial broiler chickens using trajectory-based clustering analysis
• Deep learning enhanced side channel analysis on CRYSTALS-Kyber
• Experimental integration of quantum key distribution and post-quantum cryptography in a hybrid quantum-safe cryptosystem
• PEVuln: a benchmark dataset for using machine learning to detect vulnerabilities in PE malware