Smart City Gnosys

Smart city article details

Title	Machine Learning Algorithms For Binary And Multiclass Classification Of Iot Network Traffic In Smart Cities
ID_Doc	35873
Authors	Kumar V.S.; Sunehra D.
Year	2024
Published	2024 3rd International Conference on Artificial Intelligence for Internet of Things, AIIoT 2024
DOI	http://dx.doi.org/10.1109/AIIoT58432.2024.10574674
Abstract	Internet of Things describes variety of embedded devices that are connected to the internet. This network of devices are used to build smart environment for smart city applications and used by many organizations. While data is constantly exchanged among various IoT devices, the detection of malicious attacks is essential for effective quality of service implementation in IoT networks. In this research, the University of New South Wales TON_IOT Train-Test dataset is used to classify network traffic. State of art machine learning methods such as Random Forest (RF), Decision Tree (DT), and Gradient Boosting (GB) are used to detect malicious IoT traffic. There are two classification types are considered in this study-binary and multi-label. When compared to the other two models, the Random Forest model gets the best accuracy of 99% in both binary and multi class classification. © 2024 IEEE.
Author Keywords	Accuracy; Machine learning; Network traffic; TON_IOT Train-Test

Similar Articles

Id	Similarity	Authors	Title	Published
35962	0.919	Elhaloui L.; El Filali S.; Benlahmer E.H.; Tabaa M.; Tace Y.; Rida N.	Machine Learning For Internet Of Things Classification Using Network Traffic Parameters	International Journal of Electrical and Computer Engineering, 13, 3 (2023)
9197	0.914	Janani Pandeeswari G.; Jeyanthi S.	Analysis Of Intrusion Detection Using Machine Learning Techniques	2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022 (2022)
14312	0.909	Irfan; Wildani I.M.; Yulita I.N.	Classifying Botnet Attack On Internet Of Things Device Using Random Forest	IOP Conference Series: Earth and Environmental Science, 248, 1 (2019)
24715	0.909	Ali M.; Pervez S.; Hosseini S.E.; Siddhu M.K.	Evaluation And Detection Of Cyberattack In Iot-Based Smart City Networks Using Machine Learning On The Unsw-Nb15 Dataset	International Journal of Online and Biomedical Engineering, 21, 2 (2025)
33932	0.905	Hameed A.; Leivadeas A.	Iot Traffic Multi-Classification Using Network And Statistical Features In A Smart Environment	IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD, 2020-September (2020)
28563	0.903	Ansari L.	Guarding The Future: Anomaly Detection In Iot-Enabled Smart Cities	Lecture Notes in Networks and Systems, 1110 LNNS (2024)
9646	0.903	Maniriho P.; Niyigaba E.; Bizimana Z.; Twiringiyimana V.; Mahoro L.J.; Ahmad T.	Anomaly-Based Intrusion Detection Approach For Iot Networks Using Machine Learning	CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (2020)
23835	0.901	Arabiat A.; Altayeb M.	Enhancing Internet Of Things Security: Evaluating Machine Learning Classifiers For Attack Prediction	International Journal of Electrical and Computer Engineering, 14, 5 (2024)
5689	0.9	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)
35936	0.9	Olaya M.K.P.; Martínez D.S.G.; Tejada J.A.V.; Cobo J.E.A.	Machine Learning Based Models For Detecting Attacks In Iot Systems	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 (2023)