Free use of the IoT Intrusion Datasets for academic research purposes is hereby granted in perpetuity. Please cite the following papers that have the dataset’s details.
I. Ullah and Q. H. Mahmoud, "A Technique for Generating a Botnet Dataset for Anomalous Activity Detection in IoT Networks" 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) October 11-14, 2020. Toronto, Canada.
I. Ullah and Q. H. Mahmoud, "A Scheme for Generating a Dataset for Anomalous Activity Detection in IoT Networks". In: Goutte C., Zhu X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science, vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_52.
I. Ullah and Q. H. Mahmoud, "Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks," in IEEE Access, vol. 9, pp. 103906-103926, 2021, doi: 10.1109/ACCESS.2021.3094024.
I. Ullah and Q. H. Mahmoud, "A Framework for Anomaly Detection in IoT Networks Using Conditional Generative Adversarial Networks," in IEEE Access, vol. 9, pp. 165907-165931, 2021, doi: 10.1109/ACCESS.2021.3132127.
I. Ullah and Q. H. Mahmoud, "An Anomaly Detection Model for IoT Networks based on Flow and Flag Features using a Feed-Forward Neural Network," 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), 2022, pp. 363-368, doi: 10.1109/CCNC49033.2022.9700597.