Dr Eirini Anthi
(she/her)
- Welsh speaking
- Available for postgraduate supervision
Teams and roles for Eirini Anthi
Senior Lecturer in Cyber Security
Overview
Dr. Eirini Anthi is a Senior Lecturer in Data Science and Cybersecurity at the School of Computer Science & Informatics, Cardiff University. She leads the Cybersecurity Laboratory and oversees Capture The Flag (CTF) activities, fostering the next generation of cybersecurity talent. She also teaches Operating Systems Security and Cybersecurity Operations, bringing her extensive expertise to both research and education.
Research Focus
Dr. Anthi’s research revolves around the security of Industrial Control Systems (ICS) and Internet of Things (IoT) devices, with a particular focus on using machine learning methods for intelligent cyber-attack detection. Her work also explores the robustness of machine learning models against Adversarial Machine Learning (AML) attacks, addressing critical challenges in high-stakes environments. Her research on smart home security has been featured in the Welsh Government Innovation Magazine.
Innovative Testbed Development
Dr. Anthi has been instrumental in the design and implementation of advanced cybersecurity testbeds and demonstrators. These include a state-of-the-art IoT smart home testbed for conducting real-world cybersecurity research and a cybersecurity demonstrator that combines Augmented Reality (AR) with a cutting-edge cyber range for simulating realistic network scenarios. These resources enhance both research and training at Cardiff University.
Project Leadership and Innovation
Dr. Anthi has successfully managed research projects worth approximately £300,000, including a high-impact collaboration with National Highways. This work, alongside her co-founding of TrustAI, a startup focused on ensuring AI systems are transparent and trustworthy, demonstrates her ability to translate research into real-world applications.
Awards and Recognition
Dr. Anthi’s contributions have been widely recognised. She has received accolades such as recognition by Elsevier for advancing the United Nations Sustainable Development Goals, a Best Paper Award, Best Research Poster, and 1st place in an Open-Source Intelligence competition.
Publication
2025
- Anthi, E. et al. 2025. The role of artificial intelligence in shaping intelligent motorways: opportunities, challenges, and real-world implementations. IEEE Transactions on Intelligent Transportation Systems 26 (11), pp.18325-18357. (10.1109/TITS.2025.3609972)
- Ieropoulos, V. et al. 2025. Collaborative intrusion detection in resource-constrained IoT environments: Challenges, methods, and future directions a review. Journal of Information Security and Applications 93 104127. (10.1016/j.jisa.2025.104127)
- Mohammed, A. S. et al. 2025. STADe: An unsupervised time-windows method of detecting anomalies in oil and gas Industrial Cyber-Physical Systems (ICPS) networks. International Journal of Critical Infrastructure Protection 49 100762. (10.1016/j.ijcip.2025.100762)
- Silalahi, S. et al., 2025. Interpretable ordinal-aware with contrastive-enhanced anomaly severity detection on UAV flight log messages. IEEE Access 13 , pp.105361-105379. (10.1109/access.2025.3580056)
2024
- Al Lelah, T. et al. 2024. Detecting the abuse of cloud services for C&C infrastructure through dynamic analysis and machine learning. Presented at: 2024 International Symposium on Networks, Computers and Communications (ISNCC) Washington DC, USA 22-25 October 2024. 2024 International Symposium on Networks, Computers and Communications (ISNCC). IEEE. , pp.1-7. (10.1109/isncc62547.2024.10758940)
- Anthi, E. et al. 2024. Investigating radio frequency vulnerabilities in the Internet of Things (IoT). IoT 5 (2), pp.356-380. (10.3390/iot5020018)
- Burnap, P. et al. 2024. Mapping automated cyber attack intelligence to context-based impact on system-level goals. Journal of Cybersecurity and Privacy 4 (2), pp.340-356. (10.3390/jcp4020017)
- Silalahi, S. et al., 2024. Severity-oriented multiclass drone flight logs anomaly detection. IEEE Access 12 , pp.64252 - 64266. (10.1109/ACCESS.2024.3396926)
- Williams, L. et al. 2024. Topic modelling: Going beyond token outputs. Big Data and Cognitive Computing 8 (5) 44. (10.3390/bdcc8050044)
- Williams, L. , Anthi, E. and Burnap, P. 2024. A scalable and automated framework for tracking the likely adoption of emerging technologies. Information 15 (4) 237. (10.3390/info15040237)
- Williams, L. , Anthi, E. and Burnap, P. 2024. Comparing hierarchical approaches to enhance supervised emotive text classification. Big Data and Cognitive Computing 8 (4)(10.3390/bdcc8040038)
- Williams, L. , Anthi, E. and Burnap, P. 2024. Uncovering key factors that drive the impressions of online emerging technology narratives. Information 15 (11) 706. (10.3390/info15110706)
- Williams, L. et al. 2024. Leveraging gamification and game-based learning in cybersecurity education: Engaging and inspiring non-cyber students. Journal of The Colloquium for Information Systems Security Education 11 (1)(10.53735/cisse.v11i1.186)
2023
- Al lelah, T. et al. 2023. Machine learning detection of cloud services abuse as C&C Infrastructure. Journal of Cybersecurity and Privacy 3 (4), pp.858-881. (10.3390/jcp3040039)
- Al lelah, T. et al. 2023. Abuse of cloud-based and public legitimate services as command-and-control (C&C) infrastructure: a systematic literature review. Journal of Cybersecurity and Privacy 3 (3), pp.558-590. (10.3390/jcp3030027)
- Belarbi, O. et al. 2023. Federated deep learning for intrusion detection in IoT networks. Presented at: GLOBECOM 2023 - 2023 IEEE Global Communications Conference Kuala Lumpur, Malaysia 04-08 December 2023. 2023 IEEE Global Communications Conference Proceedings. IEEE. , pp.237-242. (10.1109/GLOBECOM54140.2023.10437860)
- Mohammed, A. S. et al. 2023. Detection and mitigation of field flooding attacks on oil and gas critical infrastructure communication. Computers and Security 124 103007. (10.1016/j.cose.2022.103007)
2022
- Anthi, E. 2022. Detecting and defending against cyber attacks in a smart home Internet of Things ecosystem. PhD Thesis , Cardiff University.
- Mohammed, A. S. et al. 2022. Cybersecurity challenges in the offshore oil and gas Industry: An Industrial Cyber-Physical Systems (ICPS) perspective. ACM transactions on cyber-physical systems 6 (3), pp.1-27. 27. (10.1145/3548691)
2021
- Anthi, E. et al. 2021. A three-tiered intrusion detection system for Industrial Control Systems (ICS). Journal of Cybersecurity 7 (1) tyab006. (10.1093/cybsec/tyab006)
- Anthi, E. et al. 2021. Hardening machine learning Denial of Service (DoS) defences against adversarial attacks in IoT smart home networks. Computers and Security 108 102352. (10.1016/j.cose.2021.102352)
- Anthi, E. et al. 2021. Adversarial attacks on machine learning cybersecurity defences in industrial control systems. Journal of Information Security and Applications 58 102717. (10.1016/j.jisa.2020.102717)
- Radanliev, P. et al., 2021. Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge. Environment Systems and Decisions 41 , pp.236-247. (10.1007/s10669-020-09792-x)
2019
- Anthi, E. et al. 2019. A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things 6 (5), pp.9042-9053. (10.1109/JIOT.2019.2926365)
2018
- Anthi, E. et al. 2018. EclipseIoT: A secure and adaptive hub for the Internet of Things. Computers and Security 78 , pp.477-490. (10.1016/j.cose.2018.07.016)
2017
- Anthi, E. et al. 2017. Secure data sharing and analysis in cloud-based energy management systems. Presented at: Second EAI International Conference, IISSC 2017 and CN4IoT 2017 Brindisi, Italy 20-21 April 2017. Published in: Longo, A. et al., IISSC 2017, CN4IoT 2017: Cloud Infrastructures, Services, and IoT Systems for Smart Cities. Vol. 189.Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Springer. , pp.228-242. (10.1007/978-3-319-67636-4_24)
- Anthi, E. and Theodorakopoulos, G. 2017. Sensitive data in Smartphone Applications: Where does it go? Can it be intercepted?. Technical Report.
Articles
- Al lelah, T. et al. 2023. Machine learning detection of cloud services abuse as C&C Infrastructure. Journal of Cybersecurity and Privacy 3 (4), pp.858-881. (10.3390/jcp3040039)
- Al lelah, T. et al. 2023. Abuse of cloud-based and public legitimate services as command-and-control (C&C) infrastructure: a systematic literature review. Journal of Cybersecurity and Privacy 3 (3), pp.558-590. (10.3390/jcp3030027)
- Anthi, E. et al. 2018. EclipseIoT: A secure and adaptive hub for the Internet of Things. Computers and Security 78 , pp.477-490. (10.1016/j.cose.2018.07.016)
- Anthi, E. et al. 2025. The role of artificial intelligence in shaping intelligent motorways: opportunities, challenges, and real-world implementations. IEEE Transactions on Intelligent Transportation Systems 26 (11), pp.18325-18357. (10.1109/TITS.2025.3609972)
- Anthi, E. et al. 2021. A three-tiered intrusion detection system for Industrial Control Systems (ICS). Journal of Cybersecurity 7 (1) tyab006. (10.1093/cybsec/tyab006)
- Anthi, E. et al. 2024. Investigating radio frequency vulnerabilities in the Internet of Things (IoT). IoT 5 (2), pp.356-380. (10.3390/iot5020018)
- Anthi, E. et al. 2021. Hardening machine learning Denial of Service (DoS) defences against adversarial attacks in IoT smart home networks. Computers and Security 108 102352. (10.1016/j.cose.2021.102352)
- Anthi, E. et al. 2019. A supervised intrusion detection system for smart home IoT devices. IEEE Internet of Things 6 (5), pp.9042-9053. (10.1109/JIOT.2019.2926365)
- Anthi, E. et al. 2021. Adversarial attacks on machine learning cybersecurity defences in industrial control systems. Journal of Information Security and Applications 58 102717. (10.1016/j.jisa.2020.102717)
- Burnap, P. et al. 2024. Mapping automated cyber attack intelligence to context-based impact on system-level goals. Journal of Cybersecurity and Privacy 4 (2), pp.340-356. (10.3390/jcp4020017)
- Ieropoulos, V. et al. 2025. Collaborative intrusion detection in resource-constrained IoT environments: Challenges, methods, and future directions a review. Journal of Information Security and Applications 93 104127. (10.1016/j.jisa.2025.104127)
- Mohammed, A. S. et al. 2023. Detection and mitigation of field flooding attacks on oil and gas critical infrastructure communication. Computers and Security 124 103007. (10.1016/j.cose.2022.103007)
- Mohammed, A. S. et al. 2025. STADe: An unsupervised time-windows method of detecting anomalies in oil and gas Industrial Cyber-Physical Systems (ICPS) networks. International Journal of Critical Infrastructure Protection 49 100762. (10.1016/j.ijcip.2025.100762)
- Mohammed, A. S. et al. 2022. Cybersecurity challenges in the offshore oil and gas Industry: An Industrial Cyber-Physical Systems (ICPS) perspective. ACM transactions on cyber-physical systems 6 (3), pp.1-27. 27. (10.1145/3548691)
- Radanliev, P. et al., 2021. Dynamic real-time risk analytics of uncontrollable states in complex internet of things systems: cyber risk at the edge. Environment Systems and Decisions 41 , pp.236-247. (10.1007/s10669-020-09792-x)
- Silalahi, S. et al., 2025. Interpretable ordinal-aware with contrastive-enhanced anomaly severity detection on UAV flight log messages. IEEE Access 13 , pp.105361-105379. (10.1109/access.2025.3580056)
- Silalahi, S. et al., 2024. Severity-oriented multiclass drone flight logs anomaly detection. IEEE Access 12 , pp.64252 - 64266. (10.1109/ACCESS.2024.3396926)
- Williams, L. et al. 2024. Topic modelling: Going beyond token outputs. Big Data and Cognitive Computing 8 (5) 44. (10.3390/bdcc8050044)
- Williams, L. , Anthi, E. and Burnap, P. 2024. A scalable and automated framework for tracking the likely adoption of emerging technologies. Information 15 (4) 237. (10.3390/info15040237)
- Williams, L. , Anthi, E. and Burnap, P. 2024. Comparing hierarchical approaches to enhance supervised emotive text classification. Big Data and Cognitive Computing 8 (4)(10.3390/bdcc8040038)
- Williams, L. , Anthi, E. and Burnap, P. 2024. Uncovering key factors that drive the impressions of online emerging technology narratives. Information 15 (11) 706. (10.3390/info15110706)
- Williams, L. et al. 2024. Leveraging gamification and game-based learning in cybersecurity education: Engaging and inspiring non-cyber students. Journal of The Colloquium for Information Systems Security Education 11 (1)(10.53735/cisse.v11i1.186)
Conferences
- Al Lelah, T. et al. 2024. Detecting the abuse of cloud services for C&C infrastructure through dynamic analysis and machine learning. Presented at: 2024 International Symposium on Networks, Computers and Communications (ISNCC) Washington DC, USA 22-25 October 2024. 2024 International Symposium on Networks, Computers and Communications (ISNCC). IEEE. , pp.1-7. (10.1109/isncc62547.2024.10758940)
- Anthi, E. et al. 2017. Secure data sharing and analysis in cloud-based energy management systems. Presented at: Second EAI International Conference, IISSC 2017 and CN4IoT 2017 Brindisi, Italy 20-21 April 2017. Published in: Longo, A. et al., IISSC 2017, CN4IoT 2017: Cloud Infrastructures, Services, and IoT Systems for Smart Cities. Vol. 189.Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Springer. , pp.228-242. (10.1007/978-3-319-67636-4_24)
- Belarbi, O. et al. 2023. Federated deep learning for intrusion detection in IoT networks. Presented at: GLOBECOM 2023 - 2023 IEEE Global Communications Conference Kuala Lumpur, Malaysia 04-08 December 2023. 2023 IEEE Global Communications Conference Proceedings. IEEE. , pp.237-242. (10.1109/GLOBECOM54140.2023.10437860)
Monographs
- Anthi, E. and Theodorakopoulos, G. 2017. Sensitive data in Smartphone Applications: Where does it go? Can it be intercepted?. Technical Report.
Thesis
- Anthi, E. 2022. Detecting and defending against cyber attacks in a smart home Internet of Things ecosystem. PhD Thesis , Cardiff University.
Research
Research Interests
My research focuses on enhancing the security and trustworthiness of Industrial Control Systems (ICS) and Internet of Things (IoT) devices. I specialise in leveraging machine learning and adversarial machine learning (AML) techniques to develop intelligent, robust mechanisms for detecting and mitigating cyber-attacks. Additionally, I am exploring the application of cognitive AI architectures to cybersecurity and safe AI systems, investigating how reasoning, learning, and adaptability can improve the resilience and reliability of AI in high-stakes environments. Ensuring that AI systems are transparent, reliable, and adaptive is central to my work, as these qualities are critical for fostering trust and safe deployment.
Teaching
Over the past 4 years apart from giving a range of guest lectures, assisting with labs, and creating CTF based teaching events, I have also supervised more than 30 state-of-art research based final year projects in cyber security for both postrgaduate and undergraduate students. Few of the projects I supervised included:
- Investigating adversarial machine learning against malware detection systems
- Investigating Malware propagation via IoT devices to internal networks
- Vulnerability assessment and risk modeling of attacks in IT systems
- Detecting network based attacks in Industrial Control Systems
- Evaluating the robustness of a lattice-based cryptosystem
- Investigating the Security and Privacy of a Real-World Internet of Things Environment (During the project the student Identified and reported to the company a vulnerability against the IoT device)
- Investigation and analysis of security issues in smartphone applications
- Intelligent Attack Detection for IoT Networks (Led to publication)
- Investigating Radio Frequency vulnerabilities in the Internet of Things using HackRF (Aim to produce a publication)
- Cryptanalysis of a Wireless Security System
- Simulating the Effects of Releasing Malware into the Internet of Things
- Security Issues, Privacy, and Challenges in the Internet of Things: Vulnerabilities, Threats and Attacks.
Biography
Dr. Eirini Anthi is a Senior Lecturer in Data Science and Cybersecurity at the School of Computer Science & Informatics, Cardiff University, where she leads the Cybersecurity Laboratory and oversees Capture The Flag (CTF) activities. She teaches Operating Systems Security and Cybersecurity Operations, drawing on her extensive expertise in cybersecurity and data science. Her research focuses on the security of IoT and Industrial Control Systems, with an emphasis on developing intelligent, robust mechanisms for detecting and defending against cyber-attacks using machine learning and adversarial machine learning techniques. She also explores the security and trustworthiness of AI systems, addressing critical challenges in deploying AI in high-stakes environments.
As Principal Investigator in a high-impact £120,000 collaboration with National Highways, Dr. Anthi has led research on enhancing AI trustworthiness for Intelligent Transport Systems. She has also managed other research projects totalling approximately £300,000, delivering impactful outcomes such as publications in top-tier journals and the creation of a startup. Building on her extensive work in AI safety, Dr. Anthi is a co-founder of TrustAI, a startup developing tools to evaluate and enhance AI trustworthiness. TrustAI provides organizations with solutions to ensure AI systems are transparent, compliant, and aligned with ethical standards, fostering trust and operational efficiency.
Dr. Anthi’s contributions to cybersecurity and AI safety are also widely recognised. Her research has earned awards, including Best Paper for her work on adversarial attacks in industrial control systems and recognition from Elsevier for advancing the United Nations Sustainable Development Goals. Her papers are highly cited, with one being the third most downloaded in its field. Combining academic expertise and practical industry experience, including her tenure at Airbus as a cybersecurity researcher, Dr. Anthi is at the forefront of systemic AI safety and cybersecurity research.
Honours and awards
- Recognized by Elsevier for Advancing the United Nations Sustainable Development Goals: For impactful research on cybersecurity and AI safety.
- Best Paper Award: For research on adversarial attacks in industrial control systems, recognizing excellence in developing robust cybersecurity defenses.
- Third Most Downloaded Article in Field: Published research on adversarial machine learning and cybersecurity achieved significant global engagement and impact.
- 1st Place in Open-Source Intelligence Competition, 2019: Member of a winning team in an international competition showcasing expertise in intelligence and cybersecurity.
- Best Paper Award, 2017: Presented at the Second EAI International Conference on Cloud, Networking for IoT Systems in Italy, for outstanding research contributions.
- Best Research Poster, 2017: Awarded by Cardiff University for presenting impactful research findings in cybersecurity and AI.
Supervisions
- Internet of Things Security
- Industrial Control Systems Security
- Network Security
- Smartphone security
- Machine Learning & Adversarial Machine Learning
- Malware Analysis