APPLES: A Full Gate-Timing FPGA-Based Hardware Simulator. - Damian Dalton; Vivian Bessler; Jeffrey Griffiths; Andrew McCarthy; Abhay Vadher; Rory O'Kane; Rob Quigley; Declan O'Connor (FPL, 2003)

Computerized feature systems for identifying suspects: empirical tests using crime scenarios. - Eric S. Lee; Thomas Whalen; Michael Terris; Andrew McCarthy (Behav. Inf. Technol., 2004)

Shouting Through Letterboxes: A study on attack susceptibility of voice assistants. - Andrew McCarthy; Benedict R. Gaster; Phil Legg (Cyber Security, 2020)

Feature Vulnerability and Robustness Assessment against Adversarial Machine Learning Attacks. - Andrew McCarthy; Panagiotis Andriotis; Essam Ghadafi; Phil Legg (CyberSA, 2021)

GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs). - Emily R. Jefferson; James Liley; Maeve Malone; Smarti Reel; Alba Crespi-Boixader; Xaroula Kerasidou; Francesco Tava; Andrew McCarthy; Richard Preen; Alberto Blanco-Justicia; Esma Mansouri-Benssassi; Josep Domingo-Ferrer; et al. (CoRR, 2022)

Safe machine learning model release from Trusted Research Environments: The AI-SDC package. - Jim Smith 002; Richard John Preen; Andrew McCarthy; Alba Crespi-Boixader; James Liley; Simon Rogers (CoRR, 2022)

Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey. - Andrew McCarthy; Essam Ghadafi; Panagiotis Andriotis; Phil Legg (J. Cybersecur. Priv., 2022)

Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification. - Andrew McCarthy; Essam Ghadafi; Panagiotis Andriotis; Phil Legg (J. Inf. Secur. Appl., 2023)