Smart City Gnosys

Smart city article details

Title	An Ensemble Intrusion Detection System Based On Acute Feature Selection
ID_Doc	8068
Authors	Hariprasad S.; Deepa T.
Year	2024
Published	Multimedia Tools and Applications, 83, 3
DOI	http://dx.doi.org/10.1007/s11042-023-15788-x
Abstract	As the Internet of Things (IoT), 5G, and Artificial intelligence (AI) continue to converge, the number of security incidents and occurrences on the networks has recently increased. Since more devices are connected to IoT networks, security is becoming a major concern. Conventional intrusion detection systems (IDS) are not well suited for use in the complex lightweight IoT environment. This research paper presented an IDS for the smart city environment based on IoT- Message queuing telemetry transport (MQTT) networks that could detect attacks using shallow learning algorithms. The proposed framework has four parts (i) a smart city network model with multiple MQTT clients (sensors and IoT devices) is created with the help of hardware. (ii) Injected a flooding attack on the MQTT broker to create the IDS dataset with normal and attack features, (iii) Based on the acute feature selection algorithm to select the optmized features from the raw dataset and validated with the Jaccard coefficient. (iv) The dataset is further trained and validated using shallow learning algorithms such as extreme gradient boosting (XGB), K-nearest Neighbors (KNN) and Random forest (RF). Experimental results outperform with better attack detection rate, attack prediction rate and improved accuracy over 97% with lower redundancy using selected features. Experimental results show that the proposed approach is more vulnerable to attacks in the IoT network. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Author Keywords	Acute feature selection; Flooding attacks; IoT Security; KNN; Shallow learning

Similar Articles

Id	Similarity	Authors	Title	Published
23837	0.909	Almotairi A.; Atawneh S.; Khashan O.A.; Khafajah N.M.	Enhancing Intrusion Detection In Iot Networks Using Machine Learning-Based Feature Selection And Ensemble Models	Systems Science and Control Engineering, 12, 1 (2024)
22935	0.907	Merlin R.T.; Ravi R.	Empowering Smart City Iot Network Intrusion Detection With Advanced Ensemble Learning-Based Feature Selection	International Journal of Electrical and Electronics Research, 12, 2 (2024)
2939	0.906	Mosaiyebzadeh F.; Araujo Rodriguez L.G.; Macedo Batista D.; Hirata R.	A Network Intrusion Detection System Using Deep Learning Against Mqtt Attacks In Iot	Proceedings - 2021 IEEE Latin-American Conference on Communications, LATINCOM 2021 (2021)
57650	0.903	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)
24125	0.902	Alhowaide A.; Alsmadi I.; Alsinglawi B.	Ensemble-Based Cyber Intrusion Detection For Robust Smart City Protection	Proceedings - 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2024 (2024)
18873	0.901	Dandapat A.; Mondal B.	Design Of Intrusion Detection System Using Ga And Cnn For Mqtt-Based Iot Networks	Wireless Personal Communications, 134, 4 (2024)
8070	0.9	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	0.899	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)
36055	0.898	Ngo V.-D.; Vuong T.-C.; Van Luong T.; Tran H.	Machine Learning-Based Intrusion Detection: Feature Selection Versus Feature Extraction	Cluster Computing, 27, 3 (2024)
5688	0.895	Hamdan M.; Eldhai A.M.; Abdelsalam S.; Ullah K.; Bashir A.K.; Marsono M.N.; Kon F.; Batista D.M.	A Two-Tier Anomaly-Based Intrusion Detection Approach For Iot-Enabled Smart Cities	IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023 (2023)