Smart City Gnosys

Smart city article details

Title	Hybrid Catboost Regression Model Based Intrusion Detection System In Iot-Enabled Networks
ID_Doc	29714
Authors	Latha R.; Bommi R.M.
Year	2023
Published	Proceedings of the 9th International Conference on Electrical Energy Systems, ICEES 2023
DOI	http://dx.doi.org/10.1109/ICEES57979.2023.10110148
Abstract	To protect organization from random network attacks is highly important and that will save a massive information to be hacked in network traffic. The significant piece of framework utilized here to shield system from network dangers and guaranteeing elevated degree of safety. The huge measure of secret information, exchange information, exercises and subsequent meet-ups are transferred to the organization in current days through basic advances. Thus it is become adaptable for end clients to transfer the information securely over the cloud beyond intrusion attacks. Each time the client login to the specific organization empower the authorized person to access nodes, to acknowledge every access of the contributions for a specific timeframe is developed. The proposed system is focused on creating a robust model to detect network attacks coming as intrusion for IoT devices. The system develops a CatBoost regression model using IDS2017 dataset. The presented approach considers various attributes as key whole parameter for finding the presence of intrusion attacks over the network. The presented system achieved 92.5% of accuracy and compared with various states of art approaches. © 2023 IEEE.
Author Keywords	Cyber-attacks; Intrusion detection system; IoT attacks; Network security; smart city

Similar Articles

Id	Similarity	Authors	Title	Published
2510	0.867	Florrence J.M.; Antoinette A.; Buvaneswari S.; Wanare A.L.; Vashistha A.; Mulpuri M.; Rambabu R.	A Mathematical Model For Enhancing Cybersecurity In Iot Networks Using Lstm-Based Anomaly Detection And Optimization	Communications on Applied Nonlinear Analysis, 32, 2 (2025)
47766	0.865	Plazas Olaya M.K.; Vergara Tejada J.A.; Aedo Cobo J.E.	Securing Microservices-Based Iot Networks: Real-Time Anomaly Detection Using Machine Learning	Journal of Computer Networks and Communications, 2024 (2024)
48148	0.86	Dash P.B.; Senapati M.R.; Behera H.S.; Nayak J.; Vimal S.	Self-Adaptive Memetic Firefly Algorithm And Catboost-Based Security Framework For Iot Healthcare Environment	Journal of Engineering Mathematics, 144, 1 (2024)
3095	0.859	Rafrafi M.; Ghazel C.; Saidane L.	A New Model For Enhancing Iot Security Through Hybrid Optimization Of Intrusion Detection	2024 13th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks, PEMWN 2024 (2024)
57650	0.857	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)
2187	0.856	Gill K.S.; Dhillon A.	A Hybrid Machine Learning Framework For Intrusion Detection System In Smart Cities	Evolving Systems, 15, 6 (2024)
5689	0.856	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)
29780	0.856	Zupash; Rakha M.A.; Khan I.U.; Ouaissa M.; Ouaissa M.; Ayub M.Y.	Hybrid Model For Iot-Enabled Intelligent Towns Using The Mqtt-Iot-Ids2020 Dataset	Cyber Security for Next-Generation Computing Technologies (2024)
41565	0.856	Zhukabayeva T.; Ahmad Z.; Adamova A.; Karabayev N.; Mardenov Y.; Satybaldina D.	Penetration Testing And Machine Learning-Driven Cybersecurity Framework For Iot And Smart City Wireless Networks	IEEE Access, 13 (2025)
12772	0.853	Abu Al-Haija Q.; Al Badawi A.; Bojja G.R.	Boost-Defence For Resilient Iot Networks: A Head-To-Toe Approach	Expert Systems, 39, 10 (2022)