HEADS: Hybrid Ensemble Anomaly Detection System for Internet-of-Things Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-62495-7_14
Reference21 articles.
1. Ahmad, F., Ahmad, Z., Kerrache, C.A., Kurugollu, F., Adnane, A., Barka, E.: Blockchain in Internet-of-Things: architecture, applications and research directions. In: 2019 International Conference on Computer and Information Sciences, ICCIS 2019 (2019). https://doi.org/10.1109/ICCISCI.2019.8716450
2. Ahmad, Z., et al.: S-ADS: spectrogram image-based anomaly detection system for IoT networks. In: Proceedings - AiIC 2022: 2022 Applied Informatics International Conference: Digital Innovation in Applied Informatics During the Pandemic, pp. 105–110 (2022). https://doi.org/10.1109/AIIC54368.2022.9914599
3. Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., Faruki, P.: Network intrusion detection for IoT security based on learning techniques. IEEE Commun. Surv. Tutor. 21, 2671–2701 (2019). https://doi.org/10.1109/COMST.2019.2896380
4. Khan, A.S., Ahmad, Z., Abdullah, J., Ahmad, F.: A spectrogram image-based network anomaly detection system using deep convolutional neural network. IEEE Access 9, 87079–87093 (2021). https://doi.org/10.1109/ACCESS.2021.3088149
5. Keshk, M., Koroniotis, N., Pham, N., Moustafa, N., Turnbull, B., Zomaya, A.Y.: An explainable deep learning-enabled intrusion detection framework in IoT networks. Inf. Sci. 639, 119000 (2023). https://doi.org/10.1016/J.INS.2023.119000
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