Research Projects & Open Datasets
Explore cutting-edge research investigations and publicly available benchmark datasets produced by the UCD ASEADOS Lab, spanning Generative AI trust, IoT & SDN network intrusion detection, digital forensics, and smart health systems.
24
Active & Completed Projects
4
Public Datasets
€1M+
Competitive Funding
4
Core Research Pillars
Showing 24 of 24 projects
DiffMark: Differentiable Multi-Bit Watermarking for Diffusion Models
Proposes DiffMark, a novel differentiable multi-bit watermarking framework designed to protect copyright and ensure provenance in generative diffusion models. Resolves distortion, capacity, and robustness trade-offs through Latent Consistency Models (LCM).
EU HPC AIF Consortium
TriDetect: Multi-Modal GenAI Attribution & Verification Platform
Securing the modern digital ecosystem against synthetic media manipulation. TriDetect provides robust detection of AI-generated content, fine-grained source attribution, and court-ready evidentiary explanations.
ASEADOS Lab
Resilience of Vision Models Against Data-Free Model Extraction
Evaluates the security and resilience of state-of-the-art image classification models against Data-Free Model Extraction (DFME) attacks in MLaaS environments, developing novel defense paradigms and query-efficient attack strategies.
ASEADOS Lab (ECAI 2025)
Generalized & Robust Deepfake Detection Across Media Modalities
Pioneering both passive artifact-focused detectors and proactive perturbation defenses against hyper-realistic deepfake images, audio, and videos, optimizing cross-generator generalization and robustness against post-processing.
ASEADOS Lab
LLM Safety, Prompt Manipulation & RAG Vulnerability Analysis
Investigating adversarial vulnerabilities in Large Language Model (LLM) service providers, focusing on indirect prompt injection, poisoning attacks against Retrieval-Augmented Generation (RAG), and model alignment verification.
ASEADOS Lab (SECRYPT 2024)
ENFIELD: Cross-Generator Voice Deepfake Generalization
European collaborative project under the Horizon Europe ENFIELD consortium exploring the generalization of synthetic speech detection across diverse acoustic generator architectures and neural vocoders.
EU Horizon Europe ENFIELD Consortium
AI Framework for Efficient Review & Analysis of Prospectus Documents
Natural language processing and information extraction models tailored for regulatory compliance, automated verification, and high-precision analysis of complex financial prospectus documentation.
Enterprise & Industry Partners
ASEADOS-SDN-IoT: Hybrid Testbed Intrusion Detection Framework
A novel SDN-IoT intrusion detection dataset and benchmarking framework generated from a physical testbed integrating real IoT sensors with OpenFlow SDN telemetry. Designed to support cross-layer security research in next-generation programmable networks.
ASEADOS Lab (Internet of Things 2026)
InSDN: Attack Detection & Prevention in SDN Cloud Environments
A comprehensive framework and dataset designed to evaluate anomaly detection systems in Software-Defined Networking (SDN) cloud infrastructures, capturing diverse contemporary attack vectors (DDoS, Probe, DoS, Web attacks).
Collaborative Research with Dr. A. Jurcut
AI-Driven SDN-Enabled Security & Forensics for Unmanned Aerial Vehicles
Delivering a validated, privacy-preserving, and secure SDN-enabled AI framework for drone operations and digital forensic triage across civilian, enterprise, and defence applications.
UCD CCI Centre, EU ECTEG Group
FormIN: Forensic Readiness Methodology for IoT Network Investigation
Establishing proactive evidence preservation protocols, logging architectures, and digital triage mechanisms tailored for heterogeneous, resource-constrained Internet of Things (IoT) deployments.
ASEADOS Lab
Smart Vehicle Cybersecurity & In-Vehicle Telematics Forensics
Investigating CAN bus anomaly detection, electronic control unit (ECU) forensic artifact preservation, and cellular vehicle-to-everything (C-V2X) communication resilience against spoofing and tampering.
ASEADOS Lab
FraudLens: Graph Structural Learning for Illicit Bitcoin Investigations
An advanced graph neural network and XAI platform designed to assist law enforcement agencies and financial intelligence units (FIUs) in tracing illicit cryptocurrency flows, mixer transactions, and ransomware payments.
ASEADOS Lab & Financial Cybercrime Partners
CERBERUS: Countering Digital Exploitation & Encryption
Child Exploitation Response by Beating Encryption and Research to Unprotect Systems. Assisting European law enforcement agencies in developing high-performance computing capabilities to beat encryption and analyze evidence in high-priority investigations.
European Police Agencies & UCD CCI
EM-SCA: Electromagnetic Side-Channel Analysis for Hardware & IoT Forensics
Pioneering hardware-level side-channel measurement frameworks and datasets that inspect unintended electromagnetic emissions to classify running firmware, identify malware, and extract forensic state non-intrusively.
ASEADOS Lab (IEEE Access)
SILAS: Securing Machine Learning Models in Cybersecurity Operations
Developing adversarial-resilient machine learning architectures that resist poisoning, evasion, and model manipulation when deployed in high-stakes threat detection and cyber defence environments.
ASEADOS Lab (IEEE TIFS)
CyberForensicsONT: Semantic Knowledge Representation for Digital Forensics
Formal ontological frameworks and knowledge graph representations linking digital evidence, legal admissibility standards, investigative workflows, and case studies for cross-jurisdictional collaboration.
ASEADOS Lab (IEEE Access)
Forensic Analysis & Interception Verification for Secure Mobile Messaging
Investigating forensic artifacts, memory residency, and lawful interception possibilities in end-to-end encrypted mobile communication protocols (including WhatsApp, Signal, and Matrix/Riot).
ASEADOS Lab (FSIDI)
Blockchain Intelligence, Smart Contract Auditing & Consensus Security
Analyzing security vulnerabilities in distributed ledger technologies, smart contract execution flaws, and on-chain transactional graph patterns for cyber threat intelligence.
ASEADOS Lab (IJNM 2024)
Deep Learning Models for Cardiovascular Disease Risk Prediction
Research and clinical validation of multimodal deep learning models predicting cardiovascular disease progression from electronic health records, imaging, and physiological telemetry in collaboration with major hospital centers.
National University of Vietnam (HCM) & Hospital Partners
Blood Brother: AI-Driven Diabetes Risk Management Platform
An interactive digital health platform that empowers patients and clinical practitioners to monitor continuous metabolic indicators, predict pre-diabetic states, and personalize preventative lifestyle interventions.
ASEADOS Lab & Clinical Partners
CONSUS: Semantic Knowledge Representation in Digital Agriculture
Large-scale data analytics, IoT sensor integration, and ontological knowledge maps (AgriKMaps) developed in collaboration with Prof. M-T Kechadi to optimize crop yield, soil sustainability, and agricultural supply chain resilience.
Collaborative Research with Prof. M-T Kechadi
UrbanArk: Scalable LiDAR Point Cloud Management for Smart Cities
Collaborative research with Prof. M. Bertolotto developing high-performance spatial indexing, concept drift detection in spatial image streams, and scalable LiDAR management for urban resilience.
Collaborative Research with Prof. M. Bertolotto
CloudAtlas: Systems & Methodologies for Verifying Intelligent Systems
Architectures, compliance auditing frameworks, and formal verification methods ensuring autonomous AI systems operating in cloud environments meet rigorous standards of transparency, safety, and accountability.
ASEADOS Lab (AI Review 2026)
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