Smart City Gnosys

Smart city article details

Title	Intrusion Detection In Smart Cities: Leveraging Long Short-Term Memory Networks For Enhanced Cybersecurity
ID_Doc	33336
Authors	Alduraibi A.M.A.; Kachout M.
Year	2025
Published	Proceedings of 2025 4th International Conference on Computing and Information Technology, ICCIT 2025
DOI	http://dx.doi.org/10.1109/ICCIT63348.2025.10989465
Abstract	The use of the internet is continuously expanding, and data has become a crucial asset in today's digital landscape. Smart Cities leverage technology to develop innovative solutions for addressing urban challenges, utilizing communication and information frameworks. However, Smart City applications are susceptible to various cyberattacks, such as Probing (Prob), Denial of Service (DOS), User to Root (U2R) and Remote to Local (R2L) attacks. Prominent examples for Smart Cities include New York City, Dubai, London, Barcelona, Singapore, and Tokyo. This study introduces a deep learning-based way for attacks detection in Smart Cities by Long Short-Term Memory (LSTM) networks. The NSL-KDD dataset is utilized to assess the suggested solution, while the Synthetic Minority Over-sampling Technique (SMOTE) is activated to stem class imbalance. Experimental findings show that the model delivers high-performance metrics, achieving an accuracy of over 98.48%, a precision of 99%, a recall of 99%, and an F1-score of 98% on normalized data utilizing the SMOTE technique. These results focal point the validity of the proposed model in identifying attack traffic in Smart City environments. © 2025 IEEE.
Author Keywords	Deep learning; Intrusion detection system; long short-term memory; Smart Cities

Similar Articles

Id	Similarity	Authors	Title	Published
18136	0.911	Chinnasamy R.; Malliga S.; Sengupta N.	Deepguard: Fortifying Intrusion Detection With Lstm And Walrus Optimization For Smart-Cities	IET Conference Proceedings, 2023, 44 (2023)
9148	0.906	Munnee R.; Armoogum V.; Armoogum S.	Analysis Of Dos Attacks And Detection Techniques In Smart City Systems	4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024 (2024)
3359	0.901	Houichi M.; Jaidi F.; Bouhoula A.	A Novel Framework For Attack Detection And Localization In Smart Cities	2024 17th International Conference on Security of Information and Networks, SIN 2024 (2024)
17907	0.895	Liloja; Ranjana P.	Deep Learning Methodology For Detecting Breaches To Improve Security In Smart Cities	2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023 (2023)
45084	0.893	Elsaeidy, AA; Jagannath, N; Sanchis, AG; Jamalipour, A; Munasinghe, KS	Replay Attack Detection In Smart Cities Using Deep Learning	IEEE ACCESS, 8 (2020)
22956	0.892	Djenouri Y.; Belbachir A.N.	Empowering Urban Connectivity In Smart Cities Using Federated Intrusion Detection	2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings (2023)
1446	0.891	Rakha 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 Cities	Proceedings of International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024 (2024)
2456	0.891	Elsaeidy A.; Munasinghe K.S.; Sharma D.; Jamalipour A.	A Machine Learning Approach For Intrusion Detection In Smart Cities	IEEE Vehicular Technology Conference, 2019-September (2019)
7014	0.89	Reis M.J.C.S.	Ai-Driven Anomaly Detection For Securing Iot Devices In 5G-Enabled Smart Cities	Electronics (Switzerland), 14, 12 (2025)
19242	0.886	Iqbal M.W.; Issa G.F.; Yousif M.; Atif M.	Detection And Replay Of Distributed Denial Of Service Attacks In Smart Cities Using A Hybrid Deep Learning Approach	2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 (2023)