Stuart Millar

About

**NB If you’re interested in working at Rapid7 in AI I’d love to hear from you anytime! Please DM me. Can't accept PhD students for supervision presently though happy to connect.** At the forefront of Rapid7's AI Centre of Excellence my role as Principal Engineer is leading on AI/ML to accelerate the SOC whilst overseeing AI R&D and governance in VM, cloud, endpoint, TI and appsec. My expertise drives innovation day-to-day and is trusted at C-level for collaboration on building our AI vision and strategy at the company. I serve as a valued engineering peer to product management and marketing, ensuring a big-picture view balancing the short and longer term. This work extends to technical leadership for the AI group in Belfast, mentoring emerging talent in US and Israel, and striving to create an inclusive culture. Experienced in accelerated team recruitment with an extensive Belfast network, end-to-end hiring process design, plus forging strong relationships with academia, vendors and other partners. I authored Rapid7's first-ever deep learning research paper, winning Best Paper award at AISec, and conduct peer reviews for top-tier journals and conferences. Possess accomplished media skills as a presenter, producer and contributor with the BBC and RTE. All patents and publications: https://scholar.google.com/citations?hl=en&user=MEDpMQEAAAAJ Selected: Cyberattack Detection using Multiple Stages of Classifiers 18/129,802 Machine Learning Techniques for Associating Assets Related to Events with Addressable Computer Network Assets 18/190,589 Hashing Techniques for Associating Assets related to Events with Addressable Computer Network Assets 18/190,518 Machine Learning Model for Calculating Confidence Scores Associated with Potential Security Vulnerabilities 17/389,692 S. Millar, D. Podgurskii, D. Kuykendall, J. Martìnez-del-Rincón and P. Miller: "Optimising Vulnerability Triage in DAST with Deep Learning", ACM AISec 2022 - winner of Best Paper award S. Millar, N. McLaughlin, J. Martìnez-del-Rincón and P. Miller: "Multi-view Deep Learning for Zero-day Android Malware Detection", Journal of Information Security and Applications 2021 S. Millar, N. McLaughlin, J. Martìnez-del-Rincón, P. Miller and Z. Zhao: "DANdroid – A Multi-view Discriminative Adversarial Network for Obfuscated Android Malware Detection", ACM CODASPY 2020 R. Doriguzzi-Corin, S. Millar, S. Scott-Hayward, J. Martìnez-del-Rincón and D. Siracusa: "LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection" IEEE Transactions on Network and Service Management 2020