Smart City Gnosys

Smart city article details

Title	Securing Iot And Sdn Systems Using Deep-Learning Based Automatic Intrusion Detection
ID_Doc	47749
Authors	Elsayed R.A.; Hamada R.A.; Abdalla M.I.; Elsaid S.A.
Year	2023
Published	Ain Shams Engineering Journal, 14, 10
DOI	http://dx.doi.org/10.1016/j.asej.2023.102211
Abstract	Both Internet of Things (IoT) and Software Defined Networks (SDN) have a major role in increasing efficiency and productivity for smart cities. Despite that, they face potential security threats that need to be reduced. A new Intrusion Detection System (IDS) has become necessary to secure them. Many researchers have recently used recent techniques such as machine learning to analyze and identify the rapid growth of attacks and abnormal behavior. Most of these techniques have low accuracy and less scalability. To address this issue, this paper proposes a Secured Automatic Two-level Intrusion Detection System (SATIDS) based on an improved Long Short-Term Memory (LSTM) network. The proposed system differentiates between attack and benign traffic, identifies the attack category, and defines the type of sub-attack with high performance. To prove the efficiency of the proposed system, it was trained and evaluated using two of the most recent realistic datasets; ToN-IoT and InSDN datasets. Its performance was analyzed and compared to other IDSs. The experimental results show that the proposed system outperforms others in detecting many types of attacks. It achieves 96.35 % accuracy, 96 % detection rate, and 98.4 % precision for ToN-IoT dataset. For InSDN dataset, the results were 99.73 % accuracy, 98.6 % detection rate, and 98.9 % precision. © 2023 THE AUTHORS
Author Keywords	Deep Learning (DL); InSDN dataset; Internet of Things (IOT); Intrusion Detection System (IDS); ToN-IoT dataset

Similar Articles

Id	Similarity	Authors	Title	Published
3259	0.922	Sri vidhya G.; Nagarajan R.	A Novel Bidirectional Lstm Model For Network Intrusion Detection In Sdn-Iot Network	Computing, 106, 8 (2024)
2052	0.921	Elsayed R.; Hamada R.; Hammoudeh M.; Abdalla M.; Elsaid S.A.	A Hierarchical Deep Learning-Based Intrusion Detection Architecture For Clustered Internet Of Things	Journal of Sensor and Actuator Networks, 12, 1 (2023)
9648	0.897	Alsoufi 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 Review	Applied Sciences (Switzerland), 11, 18 (2021)
33032	0.896	Dawoud A.; Sianaki O.A.; Shahristani S.; Raun C.	Internet Of Things Intrusion Detection: A Deep Learning Approach	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020)
30732	0.895	Amine M.S.; Nada F.A.; Hosny K.M.	Improved Model For Intrusion Detection In The Internet Of Things	Scientific Reports, 15, 1 (2025)
23626	0.894	Hazman C.; Guezzaz A.; Benkirane S.; Azrour M.	Enhanced Ids With Deep Learning For Iot-Based Smart Cities Security	Tsinghua Science and Technology, 29, 4 (2024)
5144	0.893	Liao 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 Things	IEEE Access, 12 (2024)
47485	0.89	Tawfik A.T.; Abdullah S.H.; Nori A.S.; Rakha M.A.	Sdn-Enabled Intrusion Detection System Using Machine Learning And Neural Network Schemes	Smart Applications of Artificial Intelligence and Big Data (2025)
753	0.889	Sushant C.G.; Ajay V.L.; Sahay R.	A Comparative Analysis Of Deep Learning Algorithms For Intrusion Detection In Iot	Proceedings of the 2024 International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024 (2024)
52188	0.885	Husnain A.; Nguyen C.; Le N.T.	Software-Defined Iot With Machine Learning-Based Enhanced Security	Proceedings - 2023 28th Asia Pacific Conference on Communications, APCC 2023 (2023)