DANdroid: A Multi-View Discriminative Adversarial Network for Obfuscated Android Malware Detection. - Stuart Millar; Niall McLaughlin; Jesús Martínez del Rincón; Paul Miller 003; Ziming Zhao 001 (CODASPY, 2020)

Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection. - Roberto Doriguzzi Corin; Stuart Millar; Sandra Scott-Hayward; Jesús Martínez del Rincón; Domenico Siracusa (IEEE Trans. Netw. Serv. Manag., 2020)

Towards Explainable CNNs for Android Malware Detection. - Martin Kinkead; Stuart Millar; Niall McLaughlin; Philip O'Kane (ANT/EDI40, 2021)

IoT Security Challenges and Mitigations: An Introduction. - Stuart Millar (CoRR, 2021)

Multi-view deep learning for zero-day Android malware detection. - Stuart Millar; Niall McLaughlin; Jesús Martínez del Rincón; Paul Miller 003 (J. Inf. Secur. Appl., 2021)

Optimising Vulnerability Triage in DAST with Deep Learning. - Stuart Millar; Denis Podgurskii; Dan Kuykendall; Jesús Martínez del Rincón; Paul Miller 003 (AISec@CCS, 2022)

Vulnerability Detection in Open Source Software: An Introduction. - Stuart Millar (CoRR, 2022)

Android Malware Detection Using Deep Learning. - Stuart Millar; Niall McLaughlin; Jesús Martínez del Rincón; Paul Miller 003 (Artificial Intelligence and Cybersecurity, 2023)

Detecting Web Application DAST Attacks with Machine Learning. - Pojan Shahrivar; Stuart Millar; Ezzeldin Shereen (DSC, 2023)

Using LLM Embeddings with Similarity Search for Botnet TLS Certificate Detection. - Shashwat Kumar; Francis Hahn; Stuart Millar; Xinming Ou (AISec@CCS, 2024)