By Dr Nour Moustafa Abdelhameed Moustafa

Conference Papers

Moustafa NM; Creech GC; SLAY JS, 2017, 'Anomaly Detection System using Beta Mixture Models and Outlier Detection', in Das HD (ed.), School of Computer Engineering, KIIT University, presented at The International Conference on Computing Analytics and Networking (ICCAN 2017), School of Computer Engineering, KIIT University, 15 December 2017 - 16 July 2017

Moustafa NM; Creech GC; Slay JS, 2017, 'Flow Aggregator Module for Analysing Network Traffic', in Das HD (ed.), International Conference on Computing Analytics and Networking (ICCAN 2017), School of Computer Engineering, KIIT University, presented at International Conference on Computing Analytics and Networking (ICCAN 2017), School of Computer Engineering, KIIT University, 15 December 2017 - 16 July 2017

Haider WH; Hu JH; Moustafa N, 2017, 'Designing Anomaly Detection System for Cloud Servers by Frequency Domain Features of System Call Identifiers and Machine Learning', in 9th International Conference, MONAMI 2017, Springer International Publishing, Melbourne, presented at Nour Moustafa, Melbourne, 13 December 2017 - 15 December 2017, https://www.springerprofessional.de/en/designing-anomaly-detection-system-for-cloud-servers-by-frequenc/15746812

Marsden T; Moustafa N; Sitnikova E; Creech G, 2017, 'Probability Risk Identification Based Intrusion Detection System for SCADA Systems', in Hu J; Khalil I; Tari Z; Wen S (eds.), Mobile Networks and Management, Springer International Publishing, Melbourne, Australia, pp. 353 - 363, presented at 9th International Conference, MONAMI 2017, Melbourne, Australia, 13 December 2017 - 15 December 2017, http://dx.doi.org/10.1007/978-3-319-90775-8_28

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', Springer International Publishing, Melbourne, Australia, presented at 9th International Conference, MONAMI 2017, Melbourne, Australia, 13 December 2017 - 15 December 2017, https://www.springerprofessional.de/en/towards-developing-network-forensic-mechanism-for-botnet-activit/15746852

Hassan MM; Moustafa N; Sitnikova E; Creech G, 2017, 'Privacy Preservation Intrusion Detection Technique for SCADA Systems', Canberra, presented at MilCIS 2017 IEEE Stream, Canberra, 14 November 2017 - 16 November 2017

Moustafa N; Creech G; Sitnikova E; Hassan M, 2017, 'Collaborative Anomaly Detection Framework for handling Big Data of Cloud Computing', Canberra, Australia, presented at Military Communications and Information Systems Conference (MilCIS), Canberra, Australia, 13 November 2017 - 14 November 2017

Moustafa N; Slay J, 2015, 'UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)', in 2015 Military Communications and Information Systems Conference, MilCIS 2015 - Proceedings, http://dx.doi.org/10.1109/MilCIS.2015.7348942

Moustafa N; Slay J, 2015, 'A hybrid feature selection for network intrusion detection systems: Central points', in Security Research Institute, Edith Cowan University, Edith Cowan University, Joondalup Campus, Perth, Western Australia., presented at the 16th Australian Information Warfare Conference, Edith Cowan University, Joondalup Campus, Perth, Western Australia., 30 November 2015 - 02 December 2015, http://dx.doi.org/10.4225/75/57a84d4fbefbb

Moustafa NM; Slay J, 2015, 'The significant features of the UNSW-NB15 andthe KDD99 Data sets for Network IntrusionDetection Systems', IEEE, Kyoto, Japan, presented at The 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS 2015), collocated with RAID2015, Kyoto, Japan, 05 November 2015 - 05 November 2015, http://dx.doi.org/10.13140/RG.2.1.2264.4883

Moustafa N; Slay J, 2015, 'The significant features of the UNSW-NB15 and the KDD99 data sets for Network Intrusion Detection Systems', in Proceedings - 2015 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security, BADGERS 2015, Kyoto, Japan, pp. 25 - 31, presented at 2015 4th International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS), Kyoto, Japan, 05 November 2015 - 05 November 2015, http://ieeexplore.ieee.org/document/7809531/

Moustafa N; Slay J, 2015, 'Creating novel features to anomaly network Detection using DARPA-2009 data set', in European Conference on Information Warfare and Security, ECCWS, pp. 204 - 212

Conference Proceedings (Editor of)

Gallagher M; Moustafa N; Lakshika E, (eds.), 2020, 'AI 2020: Advances in Artificial Intelligence - 33rd Australasian Joint Conference, AI 2020, Canberra, ACT, Australia, November 29-30, 2020, Proceedings', Springer, Vol. 12576

Theses / Dissertations

Moustafa NM, 2017, Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic, http://handle.unsw.edu.au/1959.4/58748

Working Papers

AKL AMM; Sallam K; Chakrabortty R; Moustafa N; Ryan M; Raymond K-K, 2021, A Novel Multi-level Optimization Framework forEnhancing Cyber-Physical Defenses in Smart PowerSystems, http://dx.doi.org

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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