Al-Hawawreh, Muna, Nour Moustafa, Sahil Garg, and M.' FGMC-HADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems.' Computers & SecurityВ 96 (2020): 101906. Haider, Waqas, Nour Moustafa, Marwa Keshk, Amanda Fernandez, Kim-Kwang Raymond Choo, and Abdul Wahab.' Enhancing IoT Anomaly Detection Performance for Federated Learning.' In 2020 16th International Conference on Mobility, Sensing and Networking (MSN), pp. Weinger, Brett, Jinoh Kim, Alex Sim, Makiya Nakashima, Nour Moustafa, and K.' DAD: A Distributed Anomaly Detection system using ensemble one-class statistical learning in edge networks.' Future Generation Computer SystemsВ 118 (2021): 240-251. Moustafa, Nour, Marwa Keshk, Kim-Kwang Raymond Choo, Timothy Lynar, Seyit Camtepe, and Monica Whitty.' IoTBoT-IDS: A Novel Statistical Learning-enabled Botnet Detection Framework for Protecting Networks of Smart Cities.' Sustainable Cities and SocietyВ (2021): 103041. Ashraf, Javed, Marwa Keshk, Nour Moustafa, Mohamed Abdel-Basset, Hasnat Khurshid, Asim D.There are many new approaches in Intrusion Detection, Network Forensics and Privacy-Preservation in different systems such as networking, IoT, Industry 4.0 and cloud, that could be used and citied: ' Designing an effective network forensic framework for the investigation of botnets in the Internet of Things.' PhD diss., The University of New South Wales Australia, 2020. ' A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports.' IEEE AccessВ (2020). Koroniotis, Nickolaos, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, and Helge Janicke.' Enhancing network forensics with particle swarm and deep learning: The particle deep framework.' arXiv preprint arXiv:2005.00722В (2020). Koroniotis, Nickolaos, and Nour Moustafa.' A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework.' Future Generation Computer SystemsВ 110 (2020): 91-106. Koroniotis, Nickolaos, Nour Moustafa, and Elena Sitnikova.' Towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques.' In International Conference on Mobile Networks and Management, pp. Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Jill Slay.'Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset.' Future Generation Computer SystemsВ 100 (2019): 779-796. Koroniotis, Nickolaos, Nour Moustafa, Elena Sitnikova, and Benjamin Turnbull.To whom intent the use of the Bot-IoT dataset, the authors have to cite the following papers that has the datasetвЂ™s details. The authors have asserted their rights under the Copyright. Use for commercial purposes should be agreed by the authors. The extracted 5%, is comprised of 4 files of approximately 1.07 GB total size, and about 3 million records.ДЇree use of the Bot-IoT dataset for academic research purposes is hereby granted in perpetuity. To ease the handling of the dataset, we extracted 5% of the original dataset via the use of select MySQL queries. The dataset includes DDoS, DoS, OS and Service Scan, Keylogging and Data exfiltration attacks, with the DDoS and DoS attacks further organized, based on the protocol used. The extracted flow traffic, in csv format is 16.7 GB in size. The captured pcap files are 69.3 GB in size, with more than 72.000.000 records. The files were separated, based on attack category and subcategory, to better assist in labeling process. The datasetвЂ™s source files are provided in different formats, including the original pcap files, the generated argus files and csv files. TheВ networkВ environment incorporated a combination of normal and botnet traffic. The BoT-IoT dataset was created by designing a realistic network environment in the Cyber Range Lab of UNSW Canberra. The BoT-IoT dataset can be downloaded from HERE.В You can also use our new datasets: the TON_IoT and UNSW-NB15.

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