Smart City Gnosys

Smart city article details

Title	An Ensemble Xgboost Approach For The Detection Of Cyber-Attacks In The Industrial Iot Domain
ID_Doc	8079
Authors	Pareriya R.K.; Verma P.; Suhana P.
Year	2023
Published	Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective
DOI	http://dx.doi.org/10.1201/9781003264545-16
Abstract	The Industrial Internet of Things (IIoT) connects numerous sensors, databases, machines, actuators, and working individuals. Our lives were improved by IIoT from multiple sectors including medical services, transportation, energy, agriculture, smart cities, associated vehicles, etc. Due to the rapid increase of IIoT devices in the industry, these systems are being frequently attacked by cyber-criminals. In this chapter, an ensemble XGBoost-based implementation for cyber-attack detection in the Industrial IoT is proposed. The presented technique is fast, lightweight, and it is assessed by utilizing the UNSW-NB15 dataset. The performance or execution of the presented technique is analyzed by various performance metrics like accuracy, F1 score, precision, log loss, and ROC. The analysis shows that the final accuracy of the proposed strategy is 96.88% with a learning rate of 0.05. The rest of the performance or evaluation parameters such as the precision, F1 score, recall, and AUC ROC score were 97.64%, 96.88%, 96.40%, and 96.88, respectively. It is also compared with various other algorithms such as multi-layer perceptron, artificial neural network, decision tree, logistic regression, and random forest, which prove the dominance of the proposed approach. © 2023 selection and editorial matter, Govind P. Gupta, Rakesh Tripathi, Brij B. Gupta, and Kwok Tai Chui; individual chapters, the contributors.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
2185	0.9	Jayesh T.P.; Pandiaraj K.; Paul A.; Chandran R.R.; Menon P.P.	A Hybrid Machine Learning Approach To Anomaly Detection In Industrial Iot	ACCESS 2023 - 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (2023)
57650	0.892	Hazman C.; Guezzaz A.; Benkirane S.; Azrour M.	Toward An Intrusion Detection Model For Iot-Based Smart Environments	Multimedia Tools and Applications, 83, 22 (2024)
5689	0.891	Srivastav D.; Srivastava P.	A Two-Tier Hybrid Ensemble Learning Pipeline For Intrusion Detection Systems In Iot Networks	Journal of Ambient Intelligence and Humanized Computing, 14, 4 (2023)
19455	0.882	Celik A.F.; Saglam B.; Demirci S.	Developing Explainable Intrusion Detection Systems For Internet Of Things	16th International Conference on Information Security and Cryptology, ISCTURKEY 2023 - Proceedings (2023)
2549	0.868	Ronghua M.A.	A Method By Utilizing Deep Learning To Identify Malware Within Numerous Industrial Sensors On Iots	International Journal of Advanced Computer Science and Applications, 15, 8 (2024)
18301	0.868	Muna R.K.; Hossain M.I.; Alam M.G.R.; Hassan M.M.; Ianni M.; Fortino G.	Demystifying Machine Learning Models Of Massive Iot Attack Detection With Explainable Ai For Sustainable And Secure Future Smart Cities	Internet of Things (Netherlands), 24 (2023)
8070	0.861	Indra G.; Nirmala E.; Nirmala G.; Senthilvel P.G.	An Ensemble Learning Approach For Intrusion Detection In Iot-Based Smart Cities	Peer-to-Peer Networking and Applications, 17, 6 (2024)
3003	0.86	Gopalakrishnan B.; Purusothaman P.	A New Design Of Intrusion Detection In Iot Sector Using Optimal Feature Selection And High Ranking-Based Ensemble Learning Model	Peer-to-Peer Networking and Applications, 15, 5 (2022)
9639	0.86	Bukhari O.; Agarwal P.; Koundal D.; Zafar S.	Anomaly Detection Using Ensemble Techniques For Boosting The Security Of Intrusion Detection System	Procedia Computer Science, 218 (2022)
33333	0.86	Hanif S.; Ilyas T.; Zeeshan M.	Intrusion Detection In Iot Using Artificial Neural Networks On Unsw-15 Dataset	HONET-ICT 2019 - IEEE 16th International Conference on Smart Cities: Improving Quality of Life using ICT, IoT and AI (2019)