Intrusion Detection System in IoT Network

International Journal of Computer Science and Engineering
© 2020 by SSRG - IJCSE Journal
Volume 7 Issue 4
Year of Publication : 2020
Authors : Mohammad Dawood Momand, Dr Vikas Thada, Mr. Utpal Shrivastava

How to Cite?

Mohammad Dawood Momand, Dr Vikas Thada, Mr. Utpal Shrivastava, "Intrusion Detection System in IoT Network," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 4, pp. 11-15, 2020. Crossref,


IoT (Internet of Things) is calculated as a new pattern that allows multiple applications to different domains within the context. Thanks to its vast expansion connected to the internet, it has been waning interest in recent times. IoT implies very different network structures and device interconnections, such as interpersonal relationships, interpersonal relationships, or interconnections between objects by means of different communication methods. Services combine to form a detailed information network.


Detection, IoT Networks


[1] Abhishek, N.V., Lim, T.J., Sikdar, B. and Tandon, A., 2018, May. “An intrusion detection system for detecting compromised gateways in clustered iot networks”. In 2018 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR) (pp. 1-6). IEEE.
[2] Adriano, D.B. and Budi, W.A.C., 2018, December. “Iotbased Integrated Home Security and Monitoring System”. In Journal of Physics: Conference Series (Vol. 1140, No. 1, p. 012006). IOP Publishing.
[3] Benkhelifa, E., Welsh, T. and Hamouda, W., 2018. “A critical review of practices and challenges in intrusion detection systems for IoT: Toward universal and resilient systems”. IEEE Communications Surveys & Tutorials, 20(4), pp.3496-3509.
[4] Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C. and Faruki, P., 2019. “Network Intrusion Detection for IoT Security based on Learning Techniques”. IEEE Communications Surveys & Tutorials.
[5] Deng, L., Li, D., Yao, X., Cox, D. and Wang, H., 2018. “Mobile network intrusion detection for IoT system based on transfer learning algorithm”. Cluster Computing, pp.1- 16.
[6] Gandhi, U.D., Kumar, P.M., Varatharajan, R., Manogaran, G., Sundarasekar, R. and Kadu, S., 2018. “HIoTPOT: surveillance on IoT devices against recent threats”. Wireless personal communications, 103(2), pp.1179-1194.
[7] Hodo, E., Bellekens, X., Hamilton, A., Dubouilh, P.L., Iorkyase, E., Tachtatzis, C. and Atkinson, R., 2016, May. “Threat analysis of IoT networks using artificial neural network intrusion detection system”. In 2016 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.
[8] Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A. and Lloret, J., 2017. “Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors”, 17(9), p.1967
[9] Pacheco, J. and Hariri, S., 2016, September. “IoT security framework for smart cyber infrastructures”. In 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS* W) (pp. 242-247). IEEE.
[10] Pajouh, H.H., Javidan, R., Khayami, R., Ali, D. and Choo, K.K.R., 2016. “A two-layer dimension reduction and twotier
classification model for anomaly-based intrusion detection in IoT backbone networks”. IEEE Transactions on Emerging Topics in Computing.
[11] Pongle, P. and Chavan, G., 2015. “Real time intrusion and wormhole attack detection in internet of things”. International Journal of Computer
Applications, 121(9).
[12] Roux, J., Alata, E., Auriol, G., Nicomette, V. and Kaâniche, M., 2017, September. “Toward an intrusion detection approach for IoT based on radio communications profiling”. In 2017 13th European Dependable Computing Conference (EDCC) (pp. 147-150). IEEE.
[13] Sedjelmaci, H., Senouci, S.M. and Al-Bahri, M., 2016, May. “A lightweight anomaly detection technique for lowresource IoT devices: A game-theoretic methodology”. In 2016 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
[14] Sforzin, A., Mármol, F.G., Conti, M. and Bohli, J.M., 2016, July. “RPiDS: Raspberry Pi IDS—A Fruitful Intrusion Detection System for IoT”. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) (pp. 440- 448). IEEE.
[15] Sherasiya, T. and Upadhyay, H., 2016. “Intrusion detection system for internet of things”. Int. J. Adv. Res. Innov. Ideas Educ.(IJARIIE), 2(3).
[16] Subasi, A., Al-Marwani, K., Alghamdi, R., Kwairanga, A., Qaisar, S.M., Al-Nory, M. and Rambo, K.A., 2018, April. “Intrusion Detection in Smart Grid Using Data Mining Techniques”. In 2018 21st Saudi Computer Society National Computer Conference (NCC) (pp. 1-6). IEEE.
[17] Suresh, S., Lakshminarayan, N.C. and Eskildsen, K.G., Honeywell International Inc, 2017. “IOT enabled wireless one-go/all-go platform sensor network solution for connected home security systems”. U.S. Patent 9,565,657.
[18] Yang, K., Ren, J., Zhu, Y. and Zhang, W., 2018. “Active learning for wireless IoT intrusion detection”. IEEE Wireless Communications, 25(6), pp.19-25.