Smart City Gnosys

Smart city article details

Title	Evaluation Of Different Machine Learning Classifiers On New Iot Dataset Ciciot2023
ID_Doc	24775
Authors	Kumar A.G.; Rastogi A.; Ranga V.
Year	2024
Published	2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024
DOI	http://dx.doi.org/10.1109/ISCS61804.2024.10581375
Abstract	In today's society, Internet of Things is playing a profound role in revolutionizing a number of industries. It's use in sectors like transportation, smart home appliances, & healthcare is rapidly growing, with new services constantly being developed. Over the past decade, there has been an exponential surge in IoT connections, and this trend is expected to continue in the coming years across different domains. However, it has also exposed a new frontier for cyber-attacks. The sensitive nature of data collected by IoT devices demands stringent security measures. This paper analyzes the classification performance of various cyber-attacks in the IoT domain. For performance evaluation, the cutting-edge CICIoT2023 dataset is used [1]. The dataset encompasses a variety of attacks carried out against 105 IoT devices, which includes benign traffic, DoS and DDoS attacks, reconnaissance and information gathering, web-based attacks, spoofing, brute-force threats, and Mirai botnet attacks. This dataset encompasses wide range of cyber threats & serves as a valuable tool for understanding and mitigating IoT-related security threats. © 2024 IEEE.
Author Keywords	Datasets; Internet of Things; Intrusion Detection System; Machine Learning; Smart City

Similar Articles

Id	Similarity	Authors	Title	Published
23835	0.896	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)
32898	0.895	Kaur B.; Dadkhah S.; Shoeleh F.; Neto E.C.P.; Xiong P.; Iqbal S.; Lamontagne P.; Ray S.; Ghorbani A.A.	Internet Of Things (Iot) Security Dataset Evolution: Challenges And Future Directions	Internet of Things (Netherlands), 22 (2023)
33508	0.886	Saini K.S.; Chaudhary S.	Investigation On Attack Detection In Iot Networks: A Study And Analysis Of The Existing Machine Learning And Deep Learning Techniques	3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025)
14994	0.881	El Zegil F.; Nanda P.; Mohanty M.; Alzahrani M.	Comparative Analysis Of Intrusion Detection Schemes In Internet Of Things(Iot) Based Applications	2024 17th International Conference on Security of Information and Networks, SIN 2024 (2024)
15037	0.88	Abinaya M.; Prabakeran S.; Kalpana M.	Comparative Evaluation On Various Machine Learning Strategies Based On Identification Of Ddos Attacks In Iot Environment	2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023 (2023)
36897	0.877	Alamareen A.B.; Al-Mashagbeh M.H.; Abuasal S.; Hussein A.S.	Methodology To Improving Iot Network Security With Machine Learning Using The Iot Intrusion Dataset	Studies in Systems, Decision and Control, 572 (2025)
24715	0.876	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)
12793	0.874	Sharma A.; Babbar H.	Bot-Iot: Detection Of Attacks In Iot-Cybersecurity For Smart Transportation	Proceedings of the 8th International Conference on Communication and Electronics Systems, ICCES 2023 (2023)
34139	0.874	Selvam R.; Velliangiri S.	Iotsdl: Internet Of Things Security For Deep Learning Techniques-A Research Perspectives	2023 International Conference on Computer Communication and Informatics, ICCCI 2023 (2023)
6991	0.872	Aljohani R.; Bushnag A.; Alessa A.	Ai-Based Intrusion Detection For A Secure Internet Of Things (Iot)	Journal of Network and Systems Management, 32, 3 (2024)