Investigation of Deep Learning architectures and features for Adversarial Machine Learning Attacks in Modulation Classifications. - Marios Aristodemou; Sangarapillai Lambotharan; Gan Zheng; Leonidas Aristodemou (IVMSP, 2022)

Adversarial Poisoning Attacks on Federated Learning in Metaverse. - Marios Aristodemou; Xiaolan Liu 001; Sangarapillai Lambotharan (ICC, 2023)

Bayesian Optimization-Driven Adversarial Poisoning Attacks Against Distributed Learning. - Marios Aristodemou; Xiaolan Liu 001; Sangarapillai Lambotharan; Basil AsSadhan (IEEE Access, 2023)

Automated APT Defense Using Reinforcement Learning and Attack Graph Risk-based Situation Awareness. - Anhtuan Le; Gregory Epiphaniou; Carsten Maple; Konstantinos G. Kyriakopoulos; Lincoln Kamau Kiarie; Marios Aristodemou; Iain Phillips 002 (AutonomousCyber@CCS, 2024)

Bayesian Optimisation-Driven Poisoning Attack Against Personalised Federated Learning in Metaverse. - Marios Aristodemou; Xiaolan Liu 001; Sangarapillai Lambotharan (ICC Workshops, 2024)

Maximizing Uncertainty for Federated Learning via Bayesian Optimization-Based Model Poisoning. - Marios Aristodemou; Xiaolan Liu 001; Yuan Wang 008; Konstantinos G. Kyriakopoulos; Sangarapillai Lambotharan; Qingsong Wei (IEEE Trans. Inf. Forensics Secur., 2025)

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning. - Marios Aristodemou; Xiaolan Liu 001; Yuan Wang 008; Konstantinos G. Kyriakopoulos; Sangarapillai Lambotharan; Qingsong Wei (CoRR, 2025)