Securing IoT and SDN systems using deep-learning based automatic intrusion detection
Tài liệu tham khảo
Kumar, 2021, TP2SF: A Trustworthy Privacy-Preserving Secured Framework for sustainable smart cities by leveraging blockchain and machine learning, J Syst Archit, 115, 10.1016/j.sysarc.2020.101954
Kumar, 2021, Tripathi, An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks, Comput Commun, 166, 110, 10.1016/j.comcom.2020.12.003
Ashfaq, 2022, A review of enabling technologies for internet of medical things (IOMT) ecosystem, Ain Shams Eng J, 13, 10.1016/j.asej.2021.101660
Slama, 2021, Prosumer in smart grids based on intelligent edge computing: a review on Artificial Intelligence Scheduling Techniques, Ain Shams Eng J
Roopak, 2019, Deep learning models for cyber security in IoT networks, 0452
Doukas C. Building Internet of Things with the ARDUINO. CreateSpace Independent Publishing Platform; 2012.
da Costa, 2019, Internet of Things: a survey on machine learning-based intrusion detection approaches, Comput Netw, 151, 147, 10.1016/j.comnet.2019.01.023
Singh, 2019, A comprehensive study on APT attacks and countermeasures for future networks and communications: challenges and solutions, J Supercomput, 75, 4543, 10.1007/s11227-016-1850-4
Said Elsayed, 2020, Network anomaly detection using LSTM based autoencoder, 37
Elsayed M, Jahromi H, Nazir M, Jurcut A. The role of CNN for intrusion detection systems: an improved CNN learning approach for SDNs. In: 2020 16th IEEE international colloquium on signal processing & its applications (CSPA). IEEE; 2020. p. 29–34.
Atefi K, Hashim H, Khodadadi T. A hybrid anomaly classification with deep learning (DL) and binary algorithms (BA) as optimizer in the intrusion detection system (IDS). In: Proceedings of the 16th IEEE international colloquium on signal processing & its applications (CSPA). IEEE; 2020. p. 29–34.
Prasad, 2020, Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection, Comput Secur, 99, 10.1016/j.cose.2020.102062
Sabeel U, Heydari SS, Mohanka H, Bendhaou Y, Elgazzar K, El-Khatib K. Evaluation of deep learning in detecting unknown network attacks. In: Proceedings of international conference on smart applications, communications and networking (SmartNets). IEEE; 2019. p. 1–6.
Zhang, 2020, An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset, Comput Netw, 177, 10.1016/j.comnet.2020.107315
Kshirsagar, 2020, An ensemble feature reduction method for web-attack detection, J Discret Math Sci Cryptogr, 23, 283, 10.1080/09720529.2020.1721861
Vinayakumar, 2019, Deep learning approach for intelligent intrusion detection system, IEEE Access, 7, 41525, 10.1109/ACCESS.2019.2895334
Kumar, 2021, SP2F: a privacy-preserving framework for smart agricultural Unmanned Aerial Vehicles, Comput Netw, 10.1016/j.comnet.2021.107819
Keshk, 2019, A privacy-preserving-framework-based blockchain and deep learning for protecting smart power networks, IEEE Trans Ind Inf, 16, 5110, 10.1109/TII.2019.2957140
Haider, 2020, FGMCHADS: Fuzzy Gaussian mixture-based correntropy models for detecting zero-day attacks from linux systems, Comput Secur, 10.1016/j.cose.2020.101906
Moustafa N. ToN_IoT datasets. IEEE Dataport; 2019. Online; Accessed 10-Feb-2020. doi: 10.21227/fesz-dm97.
Elsayed, 2020, InSDN: a novel SDN intrusion dataset, IEEE Access, 8, 165263, 10.1109/ACCESS.2020.3022633