Dr Nickolaos Koroniotis

Dr Nickolaos Koroniotis

Senior Lecturer
UNSW Canberra
School of Professional Studies

NICKOLAOS KORONIOTIS received a bachelor’s degree in informatics and telematics in 2014, a master’s degree in web engineering and applications in 2016, and a PhD degree in June 2020. He enrolled in UNSW Canberra to initiate his PhD studies in February 2017 in the field of cyber security with a particular interest in network forensics and the Internet of Things. He is currently a Lecturer with the School of Professional Studies, University of New South Wales (UNSW) Canberra. His research interests lie in the fields of cybersecurity, artificial intelligence and the Internet of Things. Particularly, part research was primarily focused in investigating detection methods for network-based attacks and include the development of deep learning-based vulnerability assessment solutions for smart environments, with two particular case-studies being smart airports and the Internet of Battlefield Things.

  • Journal articles | 2024
    Mohamed H; Koroniotis N; Moustafa N; Schiliro F; Zomaya AY, 2024, 'Harnessing Federated Learning for Digital Forensics in IoT: A Survey and Introduction to the IoT-LF Framework', IEEE Open Journal of the Communications Society, pp. 1 - 1, http://dx.doi.org/10.1109/ojcoms.2024.3492919
    Journal articles | 2024
    Pasdar A; Koroniotis N; Keshk M; Moustafa N; Tari Z, 2024, 'Cybersecurity Solutions and Techniques for Internet of Things Integration in Combat Systems', IEEE Transactions on Sustainable Computing, http://dx.doi.org/10.1109/TSUSC.2024.3443256
    Journal articles | 2023
    Keshk M; Koroniotis N; Pham N; Moustafa N; Turnbull B; Zomaya AY, 2023, 'An explainable deep learning-enabled intrusion detection framework in IoT networks', Information Sciences, 639, http://dx.doi.org/10.1016/j.ins.2023.119000
    Journal articles | 2023
    Koroniotis N; Moustafa N; Schiliro F; Gauravaram P; Janicke H, 2023, 'The SAir-IIoT Cyber Testbed as a Service: A Novel Cybertwins Architecture in IIoT-Based Smart Airports', IEEE Transactions on Intelligent Transportation Systems, 24, pp. 2368 - 2381, http://dx.doi.org/10.1109/TITS.2021.3106378
    Journal articles | 2023
    Moustafa N; Koroniotis N; Keshk M; Zomaya AY; Tari Z, 2023, 'Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions', IEEE Communications Surveys and Tutorials, 25, pp. 1775 - 1807, http://dx.doi.org/10.1109/COMST.2023.3280465
    Journal articles | 2022
    Koroniotis N; Moustafa N; Slay J, 2022, 'A new Intelligent Satellite Deep Learning Network Forensic framework for smart satellite networks', Computers and Electrical Engineering, 99, http://dx.doi.org/10.1016/j.compeleceng.2022.107745
    Journal articles | 2020
    Koroniotis N; Moustafa N; Schiliro F; Gauravaram P; Janicke H, 2020, 'A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports', IEEE Access, 8, pp. 209802 - 209834, http://dx.doi.org/10.1109/ACCESS.2020.3036728
    Journal articles | 2020
    Koroniotis N; Moustafa N; Sitnikova E, 2020, 'A New Network Forensic Framework based on Deep Learning for Internet of Things Networks: A Particle Deep Framework', Future Generation Computers Systems
    Journal articles | 2019
    Koroniotis N; Moustafa N; Sitnikova E; Turnbull B, 2019, 'Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset', Future Generation Computer Systems
    Journal articles | 2019
    Koroniotis N; Moustafa N; Sitnikova E, 2019, 'Forensics and Deep Learning Mechanisms for Botnets in Internet of Things: A Survey of Challenges and Solutions', IEEE Access, 7, pp. 61764 - 61785, http://dx.doi.org/10.1109/ACCESS.2019.2916717
  • Conference Papers | 2023
    Mohamed H; Koroniotis N; Moustafa N, 2023, 'Digital Forensics based on Federated Learning in IoT Environment', in ACM International Conference Proceeding Series, pp. 92 - 101, http://dx.doi.org/10.1145/3579375.3579387
    Conference Papers | 2021
    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', in Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, pp. 887 - 894, http://dx.doi.org/10.1109/TrustCom53373.2021.00125
    Preprints | 2021
    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
    Conference Papers | 2020
    Koroniotis N; Moustafa N, 2020, 'Enhancing Network Forensics with Particle Swarm and Deep Learning: The Particle Deep Framework', Sydney, Australia, presented at 7th International Conference on Security and its Applications (CNSA 2020), Sydney, Australia, 28 March 2020 - 29 March 2020
    Preprints | 2020
    Koroniotis N; Moustafa N, 2020, Enhancing network forensics with particle swarm and deep learning: The particle deep framework, http://dx.doi.org/10.48550/arxiv.2005.00722
    Preprints | 2018
    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
    Conference Papers | 2017
    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
    Preprints | 2017
    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