Adversarial Machine Learning, Network Intrusion Detection Systems (NIDS), Red Teaming, Command and Control (C2),. defence evasion, Generative Adversarial Networks (GANs), Packet-Level Traffic Manipulation, flow-based network analysis, ML security evaluation, adversarial example transferability, MITRE ATT&CK and MITRE ATLAS, post-exploitation techniques, Stealthy Network Communications, Machine Learning robustness, cyber offense simulation

Hajizadeh, Mehrdad, Golchin, Pegah, Nowroozi, Ehsan ORCID: https://orcid.org/0000-0002-5714-8378, Rigaki, Maria, Valeros, Veronica, Garcia, Sebastian, Conti, Mauro and Bauschert, Thomas (2025) DeepRed: a deep learning-powered command and control framework for multi-stage red teaming against ML-based network intrusion detection systems. In: WOOT '25: Proceedings of the 19th USENIX WOOT Conference on Offensive Technologies. August 11–12, 2025. USENIX The Advanced Computing Systems Association, Seattle, WA, USA, pp. 103-127. ISBN 978-1939133502

Federated learning, Causative attacks, Adversarial machine learning, Corrupted training sets, Cybersecurity, Data poisoning

Nowroozi, Ehsan ORCID: https://orcid.org/0000-0002-5714-8378, Haider, Imran, Taheri, Rahim and Conti, Mauro (2025) Federated learning under attack: exposing vulnerabilities through data poisoning attacks in computer networks. IEEE Transactions on Network and Service Management. ISSN 1932-4537 (Online) (doi:10.1109/TNSM.2025.3525554)

adversarial machine learning, adversarial examples, backdoor attacks, data poisoning, model extraction, membership inference, data reconstruction, privacy attacks, federated learning security, explainable AI, probabilistic robustness, AI security, cybersecurity, denial-of-service attacks, sponge attacks, machine learning robustness, quantum adversarial AI, IoT security, cyber-physical systems, deep learning security

Nowroozi, Ehsan ORCID: https://orcid.org/0000-0002-5714-8378, Taheri, Rahim and Cordeiro, Lucas (2026) Adversarial Example Detection and Mitigation Using Machine Learning. https://doi.org/10.1007/978-3-031-99447-0 . Springer Nature, Cham, Switzerland. ISBN 978-3031994463

adversarial machine learning, cyber security, machine learning

Nowroozi, Ehsan ORCID: https://orcid.org/0000-0002-5714-8378, Mohammadreza, Mohammadi, Rahdari, Ahmad, Taheri, Rahim and Conti, Mauro (2025) A random deep feature selection approach to mitigate transferable adversarial attacks. IEEE Transactions on Network and Service Management. ISSN 1932-4537 (Online) (doi:10.1109/TNSM.2025.3594253)

autoencoders, anomaly detection, federated learning, smart grid, data privacy, cyber-security

Shrestha, Rakesh, Mohammadi, Mohammadreza, Sinaei, Sima, Salcines, Alberto, Pampliega, David, Clemente, Raul, Lourdes Sanz, Ana, Nowroozi, Ehsan ORCID: https://orcid.org/0000-0002-5714-8378 and Lindgren, Andres (2024) Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid. Journal of Parallel and Distributed Computing, 193:104951. ISSN 0743-7315 (Print), 1096-0848 (Online) (doi:10.1016/j.jpdc.2024.104951)

distributed cloud computing, edge computing, privacy-preserving computing, federated Learning; Multi-Party Computation; Differential Privacy; Trusted Execution Environments

Rahdari, Ahmad, Keshavarz, Elham, Nowroozi, Ehsan ORCID: https://orcid.org/0000-0002-5714-8378, Taheri, Rahim, Hajizadeh, Mehrdad, Mohammadi, Mohammadreza, Sinaei, Sima and Bauschert, Thomas (2025) A survey on privacy and security in distributed cloud computing: exploring federated learning and beyond. IEEE Open Journal of the Communications Society. ISSN 2644-125X (Online) (doi:10.1109/OJCOMS.2025.3560034)