310d87fd-67e4-41c2-87ca-e0adc27a738c beie director@blueeyesintelligence.org 10.35940/ijitee.H9179.0711822 10.1016/j.jnca.2019.102481SenguptaJ. Sushmita R. and Das S. B. A comprehensive survey on attacks, security issues and blockchain solutions for iot and iiot. Journal of Net. and Comp. App., 149:102481, 2020. [CrossRef]10.1007/s10586-021-03286-4GhofraneFersi. Fog computing and iot in one building block: a survey and an overview of interacting techs. The Cluster Computing, 24, 12 2021. [CrossRef]B. Flavio and Milito R. Fog computing and its role in the iot. The Proceedings of the MCC workshop on Mobile Cloud Computing, 08 2012.10.1145/2677046.2677052L. Vaquero and L.Rodero M. Finding your way in the fog: Towards comprehensive def. of fog comp. The HP Laboratories Technical Report, 44, 01 2014. [CrossRef]Lei S. Choudhary N. Kumar V. Maglaras L. MukherjeeM., MatamR. and Ferrag M. A. Sec. and privacy in fog comp.: Challenges. IEEE Acc, PP, 09 2017.10.36548/jismac.2020.4.002Dr.WangandDr.BasarSmys S. Hybrid ids for iot. J. of ISMAC, 2:190-199, 09 2020. [CrossRef]Pahl M-O, Aubet F-X All eyes on you: distributed multi dimensional IoT microservice anomaly detection, pp 72-80. IEEE10.1007/s10844-015-0388-xPajouh HH, Dastghaibyfard G, Hashemi S Two-tier network anomaly detection model: a machine learning approach. J Intell Inf Syst 48(1):61-74 [CrossRef]10.1080/19393555.2015.1125974Moustafa N, Slay J The evaluation of network anomaly detec tion systems: statistical analysis of the UNSW-NB15 data set and the comparison with the kdd99 data set. Inf Secur J Glob Perspect 25(1-3):18-31 [CrossRef]Pajouh HH, Javidan R, Khayami R, Ali D, Choo KKR A two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Transactions on Emerging Topics in ComputingKumar V, Das AK, Sinha D UIDS: a unifed intrusion detection system for IoT environment. Evol Intell. https://doi.org/10.1007/ s12065-019-00291-wAut Control Comp SciKumar55137202110.3103/S0146411621020085Design of Anomaly-Based Intrusion Detection System Using Fog Computing for IoT NetworkPrabhat Kumar, Gupta, G.P. & Tripathi, R. Design of Anomaly-Based Intrusion Detection System Using Fog Computing for IoT Network. Aut. Control Comp. Sci. 55, 137-147 (2021). [CrossRef]10.1016/j.future.2017.08.043Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for internet of things. Future Gener Comput Syst 82:761-768 [CrossRef]10.1016/j.future.2017.09.056Papamartzivanos D, Mármol FG, Kambourakis G Dendron: genetic trees driven rule induction for network intrusion detection systems. Future Gener Comput Syst 79:558-574 [CrossRef]10.1109/JCN.2018.000041Prabavathy S, Sundarakantham K, Shalinie SM Design of cognitive fog computing for intrusion detection in internet of things. J Commun Netw 20(3):291-298 [CrossRef]Kumar, Prabhat & Gupta, Govind & Tripathi, Rakesh. . A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks. Journal of Ambient Intelligence and Humanized Computing.10.1186/s40537-020-00379-6Kasongo, S.M., Sun, Y. Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset. J Big Data 7, 105 (2020). [CrossRef]10.1109/ACCESS.2019.2899721Khan FA, Gumaei A, Derhab A, Hussain A A novel two-stage deep learning model for efcient network intrusion detection. IEEE Access 7:30373-30385 [CrossRef]10.1109/ACCESS.2020.2973730K. Jiang, W. Wang, A. Wang and H. Wu, "Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network," in IEEE Access, vol. 8, pp. 32464-32476, 2020, doi: 10.1109/ACCESS.2020.2973730. [CrossRef]100059Hasan201910.1016/j.iot.2019.100059Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning ApproachesHasan, Mahmudul & Islam, Md & Islam, Ishrak & Hashem, M.M.A. (2019). Attack and Anomaly Detection in IoT Sensors in IoT Sites Using Machine Learning Approaches. 100059. 10.1016/j.iot.2019.100059. [CrossRef]