## 4 The Proposed MRMR-SATIDS System
### Applying SATIDS to an IoT system
(2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] The role of CNN for intrusion detection systems: An improved CNN learning approach for SDNs. In _International Conference on Future Access Enblers of Ubiquitous and Intelligent Infrastructures_; Springer: Cham, Switzerland, 2021; pp. 91-104. Available online: [[https://link.springer.com/chapter/10.1007/978-3-030-78459-1_7](https://link.springer.com/chapter/10.1007/978-3-030-78459-1_7)]([https://link.springer.com/chapter/10.1007/978-3-030-78459-1_7](https://link.springer.com/chapter/10.1007/978-3-030-78459-1_7)) (accessed on 6 December 2022). * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] DeBot: A deep learning-based model for bot detection in industrial internet-of-things. _Comput. Electr. Eng._**2022**, _102_, 108214. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Block chain fostered cycle-consistent generative adversarial network framework espoused intrusion detection for protecting IoT network. _Trans. Emerg. Telecommun. Technol._**2022**, e4578. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Efficient Intrusion Detection System for IoT Environment. _Int. J. Adv. Comput. Sci. Appl._**2022**, _13_. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Recurrent deep learning-based feature fusion ensemble meta-classifier approach for intelligent network intrusion detection system. _Comput. Electr. Eng._**2022**, _102_, 108156. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] SP2F: A secured privacy-preserving framework for smart agricultural Unmanned Aerial Vehicles. _Comput. Netw._**2021**, _187_, 107819. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Optimizing filter-based feature selection method flow for intrusion detection system. _Electronics_**2020**, \(9\), 2114. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] On some aspects of minimum redundancy maximum relevance feature selection. _Sci. China Inf. Sci._**2020**, _63_, 112103. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Minimum redundancy maximum relevance (MRMRM) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. _Multimed. Tools Appl._**2020**, _79_, 23633-23643. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A review of artificial fish swarm algorithms: Recent advances and applications. _artif. Intell. Rev._**2022**. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON]; [PERSON] Energy-efficient routing techniques for wireless sensors networks. In _Sensor Technology: Concepts, Methodologies, Tools, and Applications_; IGI Global: Hershey, PA, USA, 2020; pp. 917-944. * [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Energy aware optimized hierarchical routing technique for wireless sensor networks. In Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Egypt, 22-24 February 2018; Springer: Cham, Switzerland, 2018; pp. 614-623. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] [PERSON] A Comprehensive Survey on Routing and Security in Mobile Wireless Sensor Networks. _Int. J. Electron. Telecommun._**2021**, _67_, 379-384. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] PPSF: A privacy-preserving and secure framework using blockchain-based machine-learning for IoT-driven smart cities. _IEEE Trans. Netw. Sci. Eng._**2021**, \(8\), 2326-2341. [CrossRef]
* Welcome to the Adversarial Robustness Toolbox. Available online: [[https://adversarial-robustness-toolbox.readthedocs.io/en/latest/](https://adversarial-robustness-toolbox.readthedocs.io/en/latest/)]([https://adversarial-robustness-toolbox.readthedocs.io/en/latest/](https://adversarial-robustness-toolbox.readthedocs.io/en/latest/)) (accessed on 6 December 2022).