Securing the intelligence at the edge.
AIoT and Cybersecurity Lab conducts applied research at the intersection of Artificial Intelligence, Internet of Things, and Cybersecurity — building intelligent edge devices and the defenses that protect them.
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Who we are
A research lab at the intersection of AI, IoT, and Cybersecurity.
We are a research lab focused on Internet of Things (IoT), AI for IoT, IoT Security, AI for Cybersecurity, and Intelligent Systems. Our work spans building AI-powered edge devices — from autonomous robotic arms to predictive maintenance platforms — and researching the security challenges these intelligent systems create, including intrusion detection, adversarial robustness, federated learning, and behavioral security monitoring. Students and researchers in our lab work hands-on with real hardware and publish research that matters.
More about the labRigorous, reproducible security research
Real-world network, IoT, and healthcare deployments
Explainable AI, from theory to detection in production
Open datasets and tooling for the community
Research
Five research streams, one mission
We organise our work into focused streams that span the AIoT stack — from silicon to the cloud, and from attack to defense.
IoT & Edge Systems
Sensor networks, embedded platforms, and edge computing architectures for real-world IoT deployments.
AI for IoT
Computer vision, TinyML, autonomous robotics, and agentic edge AI running directly on constrained devices.
IoT Security
Behavioral detection, anomaly monitoring, and privacy-by-design for connected devices.
AI for Cybersecurity
Intrusion detection, adversarial ML, and federated learning security, powered by explainable and causal AI.
Intelligent Systems
Human-AI collaboration, AI governance, and applied research at scale.
Featured work
Selected research projects
A snapshot of what the lab is building and publishing right now.
Graph visualization of network traffic flows with anomalous nodes highlighted
AI for Cybersecurity
Causal & Graph-Based Intrusion Detection
Detecting network and host intrusions with causal-mechanism modelling and graph-temporal fusion networks.
SHAP-style feature attribution chart over a network alert dashboard
AI for Cybersecurity
Explainable AI for Security Operations
Studying how well post-hoc explanation methods actually serve analysts working with deep learning intrusion detectors.
Traffic classification dashboard showing malware family clusters
IoT Security
IoT Botnet & Ransomware Classification
Classifying IoT botnet traffic and ransomware families with graph neural networks, Kolmogorov-Arnold networks, and lightweight hashing.
Join us
Come build the security foundations of AIoT with us.
We're always looking for driven master's and bachelor's students, as well as research collaborators from academia and industry.