By Associate Professor Nour Moustafa

Preprints

Luay M; Layeghy S; Hosseininoorbin S; Sarhan M; Moustafa N; Portmann M, 2026, Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems, http://dx.doi.org/10.48550/arxiv.2503.04404

Hassanin M; Moustafa N; Deng W; Radwan I, 2026, Progressive Split Mamba: Effective State Space Modelling for Image Restoration, http://dx.doi.org/10.48550/arxiv.2603.09171

Pasdar A; Kermanshahi SK; Moustafa N; Pham V-T, 2026, Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT, http://dx.doi.org/10.48550/arxiv.2602.16098

Wu P; Sun N; Moustafa N; Qu Y; Ding M, 2026, DEFENDCLI: {Command-Line} Driven Attack Provenance Examination, http://dx.doi.org/10.48550/arxiv.2508.12553

Hassanin M; Moustafa N, 2024, A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions, http://dx.doi.org/10.48550/arxiv.2405.14487

Sarhan M; Layeghy S; Moustafa N; Gallagher M; Portmann M, 2022, Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks, http://dx.doi.org/10.48550/arxiv.2108.12722

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

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; Ahmed M; Ahmed S, 2020, Data Analytics-enabled Intrusion Detection: Evaluations of ToN_IoT Linux Datasets, http://dx.doi.org/10.48550/arxiv.2010.08521

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; Moustafa N; Yang S; Guo H, 2020, Densely Connected Residual Network for Attack Recognition, http://dx.doi.org/10.48550/arxiv.2008.02196

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

Moustafa N, 2019, A Systemic IoT-Fog-Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing, http://dx.doi.org/10.48550/arxiv.1906.01055

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

Dilini N; Sun N; Miao S; Moustafa N, A Comprehensive Review on Graph-Based Anomaly Detection: Approaches for Intrusion Detection, http://dx.doi.org/10.20944/preprints202601.1466.v1

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

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