Select Publications

Preprints

Sarhan M; Layeghy S; Moustafa N; Portmann M, 2022, Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection, , http://dx.doi.org/10.21203/rs.3.rs-1631421/v1

Koroniotis N; Moustafa N; Turnbull B; Schiliro F; Gauravaram P; Janicke H, 2021, A Deep Learning-based Penetration Testing Framework for Vulnerability Identification in Internet of Things Environments, , http://dx.doi.org/10.48550/arxiv.2109.09259

Sarhan M; Layeghy S; Moustafa N; Gallagher M; Portmann M, 2021, Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks, , http://dx.doi.org/10.48550/arxiv.2108.12722

Yang S; Guo H; Moustafa N, 2021, Hunter in the Dark: Discover Anomalous Network Activity Using Deep Ensemble Network, , http://dx.doi.org/10.48550/arxiv.2105.09157

Oseni A; Moustafa N; Janicke H; Liu P; Tari Z; Vasilakos A, 2021, Security and Privacy for Artificial Intelligence: Opportunities and Challenges, , http://dx.doi.org/10.48550/arxiv.2102.04661

Hassanin M; Moustafa N; Tahtali M, 2020, A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models, , http://dx.doi.org/10.48550/arxiv.2012.04734

Hassanin M; Radwan I; Moustafa N; Tahtali M; Kumar N, 2020, Mitigating the Impact of Adversarial Attacks in Very Deep Networks, , http://dx.doi.org/10.48550/arxiv.2012.04750

Sarhan M; Layeghy S; Moustafa N; Portmann M, 2020, NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems, , http://dx.doi.org/10.48550/arxiv.2011.09144

Moustafa N; Keshk M; Debie E; Janicke H, 2020, Federated TON_IoT Windows Datasets for Evaluating AI-based Security Applications, , http://dx.doi.org/10.48550/arxiv.2010.08522

Wu P; Guo H; Moustafa N, 2020, Pelican: A Deep Residual Network for Network Intrusion Detection, , http://dx.doi.org/10.48550/arxiv.2001.08523

Koroniotis N; Moustafa N; Sitnikova E; Turnbull B, 2018, Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset, , http://dx.doi.org/10.48550/arxiv.1811.00701

Moustafa N; Creech G; Sitnikova E; Keshk M, 2017, Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing, , http://dx.doi.org/10.48550/arxiv.1711.02829

Keshk M; Moustafa N; Sitnikova E; Creech G, 2017, Privacy Preservation Intrusion Detection Technique for SCADA Systems, , http://dx.doi.org/10.48550/arxiv.1711.02828

Marsden T; Moustafa N; Sitnikova E; Creech G, 2017, Probability Risk Identification Based Intrusion Detection System for SCADA Systems, , http://dx.doi.org/10.48550/arxiv.1711.02826

Moustafa N; Slay J, 2017, RCNF: Real-time Collaborative Network Forensic Scheme for Evidence Analysis, , http://dx.doi.org/10.48550/arxiv.1711.02824

Koroniotis N; Moustafa N; Sitnikova E; Slay J, 2017, Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques, , http://dx.doi.org/10.48550/arxiv.1711.02825

Moustafa N; Slay J, 2017, A hybrid feature selection for network intrusion detection systems: Central points, , http://dx.doi.org/10.48550/arxiv.1707.05505


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