| LUCID: A practical, lightweight deep learning solution for DDoS attack detection R Doriguzzi-Corin, S Millar, S Scott-Hayward, J Martinez-del-Rincon, ... IEEE Transactions on Network and Service Management 17 (2), 876-889, 2020 | 357 | 2020 |
| Multi-view deep learning for zero-day Android malware detection S Millar, N McLaughlin, JM del Rincon, P Miller Journal of Information Security and Applications 58, 102718, 2021 | 107 | 2021 |
| Dandroid: A multi-view discriminative adversarial network for obfuscated android malware detection S Millar, N McLaughlin, J Martinez del Rincon, P Miller, Z Zhao Proceedings of the tenth ACM conference on data and application security and …, 2020 | 78 | 2020 |
| Towards explainable CNNs for Android malware detection M Kinkead, S Millar, N McLaughlin, P O’Kane Procedia Computer Science 184, 959-965, 2021 | 67 | 2021 |
| IoT Security Challenges and Mitigations: An Introduction S Millar arXiv preprint arXiv:2112.14618, 2016 | 13* | 2016 |
| Android Malware Detection using Deep Learning S Millar, N McLaughlin, J Martìnez-del-Rincón, P Miller Artificial Intelligence and Cybersecurity: Theory and Applications, 2022 | 5 | 2022 |
| Optimising Vulnerability Triage in DAST with Deep Learning S Millar, D Podgurskii, D Kuykendall, J Martínez-del-Rincón, P Miller ACM AISEC co-located with the 29th ACM Conference on Computer and …, 2022 | 4 | 2022 |
| A cyber security risk assessment of hospital infrastructure including TLS/SSL and other threats S Millar Queen's University Belfast, 2016 | 4 | 2016 |
| Vulnerability Detection in Open Source Software: An Introduction S Millar arXiv preprint arXiv:2203.16428, 2022 | 3 | 2022 |
| Detecting Web Application DAST Attacks with Machine Learning P Shahrivar, S Millar, E Shereen IEEE DSC 2023 AI/ML Workshop, 2023 | 2 | 2023 |
| Hashing techniques for verifying correctness of associations between assets related to events and addressable computer network assets S Millar, R McTeggart US Patent App. 18/190,371, 2024 | 1 | 2024 |
| Machine Learning Techniques for Associating Assets Related to Events with Addressable Computer Network Assets S Millar, R McTeggart US Patent App. 18/190,589, 2023 | 1 | 2023 |
| MODEL-BASED CONFIDENCE RANKING OF WEB APPLICATION VULNERABILITIES S Millar, D Podgurskii US Patent App. 18/883,033, 2025 | | 2025 |
| Machine learning techniques for associating assets related to events with addressable computer network assets S Millar, R McTeggart US Patent 12,143,505, 2024 | | 2024 |
| Using LLM Embeddings with Similarity Search for Botnet TLS Certificate Detection K Shashwat, F Hahn, S Millar, X Ou ACM AISEC co-located with the 31st ACM Conference on Computer and …, 2024 | | 2024 |
| Detecting Web Application DAST Attacks in Large-Scale Event Data P Shahrivar, S Millar Artificial Intelligence for Security: Enhancing Protection in a Changing …, 2024 | | 2024 |
| Machine learning techniques for verifying correctness of associations between assets related to events and addressable computer network assets S Millar, R McTeggart US Patent App. 18/190,456, 2024 | | 2024 |
| Anomalous Vulnerability Management Content Detection S Millar, G Finnbogason US Patent App. 18/338,259, 2023 | | 2023 |
| Application-level Cybersecurity Using Multiple Stages of Classifiers P Shahrivar, S Millar US Patent App. 18/129,809, 2023 | | 2023 |
| Cyberattack Detection using Multiple Stages of Classifiers P Shahrivar, S Millar US Patent App. 18/129,802, 2023 | | 2023 |
Jesús Martínez del RincónProfessor at Queen's University BelfastVerified email at qub.ac.uk
