Smart City Gnosys

Smart city article details

Title A Comprehensive Study Of Intrusion Detection Within Internet Of Things-Based Smart Cities: Synthesis, Analysis And A Novel Approach
ID_Doc 957
Authors Houichi M.; Jaidi F.; Bouhoula A.
Year 2023
Published 2023 International Wireless Communications and Mobile Computing, IWCMC 2023
DOI http://dx.doi.org/10.1109/IWCMC58020.2023.10182948
Abstract In order to improve the quality of human existence, comfort and efficiency are key objectives in smart environments. It is now possible to construct smart cities due to the latest advancements in Internet of Things (IoT) technology. Privacy and security are major concerns in IoT-based smart objects. Smart environments are at risk for safety from IoT-based technologies. Intrusion detection systems (IDSs) created for IoT environments are essential for preventing IoT-related security threats. Many cyber security systems use IDSs to find intrusions. Anomaly-based IDS learns the typical pattern of system activity and alerts on anomalous events as they happen as opposed to analyzing monitored events against a database of known intrusion events, as is the case with signature-based IDS. The installation of IDS on the IoT network is the main topic of this paper. Key design approach presented in this paper must be taken into consideration when developing an intrusion detection system for the Internet of Things. In this study, we use the Convolutional Neural Network (CNN) to identify attacks on nine commercial IoT devices. Using an actual N-BaIoT dataset that was taken from a real system and included both benign and harmful patterns, extensive empirical research was conducted. The testing results demonstrated a good accuracy of the CNN model in identifying botnet assaults from security cameras with accuracies of 90.25% and 91.76%. Overall, the CNN model was effective in accurately identifying botnet attacks from a variety of IoT devices. © 2023 IEEE.
Author Keywords Cyber Security; Internet of things; Intrusion Detection; Security and Privacy; Smart Citiy


Similar Articles


Id Similarity Authors Title Published
23626 View0.933Hazman C.; Guezzaz A.; Benkirane S.; Azrour M.Enhanced Ids With Deep Learning For Iot-Based Smart Cities SecurityTsinghua Science and Technology, 29, 4 (2024)
1446 View0.927Rakha M.A.; Akbar A.; Chhabra G.; Kaushik K.; Arshi O.; Khan I.U.A Detailed Comparative Study Of Ai-Based Intrusion Detection System For Smart CitiesProceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024 (2024)
5688 View0.927Hamdan 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 CitiesIEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023 (2023)
57650 View0.925Hazman C.; Guezzaz A.; Benkirane S.; Azrour M.Toward An Intrusion Detection Model For Iot-Based Smart EnvironmentsMultimedia Tools and Applications, 83, 22 (2024)
5144 View0.924Liao H.; Murah M.Z.; Hasan M.K.; Aman A.H.M.; Fang J.; Hu X.; Khan A.U.R.A Survey Of Deep Learning Technologies For Intrusion Detection In Internet Of ThingsIEEE Access, 12 (2024)
9648 View0.922Alsoufi M.A.; Razak S.; Siraj M.M.; Nafea I.; Ghaleb F.A.; Saeed F.; Nasser M.Anomaly-Based Intrusion Detection Systems In Iot Using Deep Learning: A Systematic Literature ReviewApplied Sciences (Switzerland), 11, 18 (2021)
5274 View0.921Nandhini N.; Manikandan V.; Manavaalan G.; Elango S.; Jeevakarunya C.; Kumar P.V.A Survey On Intrusion Detection System In Smart City: Security Concerns2023 7th International Conference on Computer Applications in Electrical Engineering-Recent Advances: Sustainable Transportation Systems, CERA 2023 (2023)
8304 View0.921Selvam R.; Velliangiri S.An Improving Intrusion Detection Model Based On Novel Cnn Technique Using Recent Cic-Ids DatasetsInternational Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024 (2024)
30732 View0.916Amine M.S.; Nada F.A.; Hosny K.M.Improved Model For Intrusion Detection In The Internet Of ThingsScientific Reports, 15, 1 (2025)
34132 View0.915Ashraf, J; Keshk, M; Moustafa, N; Abdel-Basset, M; Khurshid, H; Bakhshi, AD; Mostafa, RRIotbot-Ids: A Novel Statistical Learning-Enabled Botnet Detection Framework For Protecting Networks Of Smart CitiesSUSTAINABLE CITIES AND SOCIETY, 72 (2021)