Items where Author is "Nowroozi, Ehsan"

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. defence evasion 1.5C classifier adversarial attacks adversarial example transferability adversarial examples adversarial machine learning adversarial robustness AI security anomaly detection attack success rate (ASR) autoencoder autoencoders backdoor attacks binary classification Causative attacks Command and Control (C2) computer network security convolutional neural networks (CNN) Corrupted training sets cyber offense simulation cyber security cyber-physical systems cyber-security cybersecurity data poisoning data privacy data reconstruction Deep learning deep learning security denial-of-service attacks dense fusion layer Differential Privacy distributed cloud computing edge computing ensemble learning explainable AI federated Learning Federated learning federated learning security flow-based network analysis Generative Adversarial Networks (GANs) intrusion detection IoT security machine learning machine learning robustness machine learning security membership inference MITRE ATT&CK and MITRE ATLAS ML security evaluation model extraction Multi-Party Computation N-BaIoT dataset Network Intrusion Detection Systems (NIDS) one-class classification Packet-Level Traffic Manipulation post-exploitation techniques privacy attacks privacy-preserving computing probabilistic robustness quantum adversarial AI Red Teaming RIPE-Atlas dataset smart grid sponge attacks Stealthy Network Communications Trusted Execution Environments

Number of items: 75.

. defence evasion

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

1.5C classifier

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

adversarial attacks

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

adversarial example transferability

adversarial examples

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

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)

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 robustness

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

AI security

anomaly detection

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network 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)

attack success rate (ASR)

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

autoencoder

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

autoencoders

backdoor attacks

binary classification

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

Causative attacks

Command and Control (C2)

computer network security

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

convolutional neural networks (CNN)

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

Corrupted training sets

cyber offense simulation

cyber security

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

cyber-physical systems

cyber-security

cybersecurity

data poisoning

data privacy

data reconstruction

Deep learning

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

deep learning security

denial-of-service attacks

dense fusion layer

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

Differential Privacy

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)

distributed cloud computing

edge computing

ensemble learning

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

explainable AI

federated Learning

Federated learning

federated learning security

flow-based network analysis

Generative Adversarial Networks (GANs)

intrusion detection

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

IoT security

machine learning

machine learning robustness

machine learning security

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

membership inference

MITRE ATT&CK and MITRE ATLAS

ML security evaluation

model extraction

Multi-Party Computation

N-BaIoT dataset

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

Network Intrusion Detection Systems (NIDS)

one-class classification

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .

Packet-Level Traffic Manipulation

post-exploitation techniques

privacy attacks

privacy-preserving computing

probabilistic robustness

quantum adversarial AI

Red Teaming

RIPE-Atlas dataset

Bahçeşehir Üniversitesi (Bahçeşehir University) - Dr. Ehsan Nowroozi (2024) Patent TR2023004922A1: Deep learning method to strengthen computer network security. .