Smart City Gnosys

Smart city article details

Title	Anomaly Detection Algorithm Of Industrial Internet Of Things Data Platform Based On Deep Learning
ID_Doc	9610
Authors	Li X.; Xie C.; Zhao Z.; Wang C.; Yu H.
Year	2024
Published	IEEE Transactions on Green Communications and Networking, 8, 3
DOI	http://dx.doi.org/10.1109/TGCN.2024.3403102
Abstract	The development of the Internet of Things (IoT) causes most industrial applications to utilize IoT devices to improve their productivity. Applications such as smart cities, energy management, smart homes, smart cars, and supply chain management widely utilize the IoT to manage the industries' efficiency. Industrial IoT devices are frequently affected by cybercriminals and damage information and productivity. Criminal activities can be overcome by applying various machine-learning techniques. Existing methods can process intermediate attacks; however, traditional machine learning techniques have difficulties predicting adversarial and catastrophic attacks. In addition, most of the AI-based industrial applications have heterogeneous and mixed data, requiring robust intruder detection systems. The research issues are addressed by introducing the Meta-Heuristic Optimized Deep Random Neural Networks (MH-DRNN). The system uses the optimization process in feature selection and classification, reducing the heterogeneous data analysis issues. The optimization method selects the features from the feature set according to the sunflower movement, which minimizes the difficulties in computation. In addition, three MLP and three recurrent layers are incorporated into this system to maximize the prediction rate up to 99.2% accuracy. © 2017 IEEE.
Author Keywords	Industrial Internet of Things (IIoT); intermediate attacks; machine learning; meta-heuristic optimized deep random neural networks; sunflower movement

Similar Articles

Id	Similarity	Authors	Title	Published
9648	0.896	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	0.893	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)
5144	0.882	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)
17825	0.881	Kadheem Hammood B.A.; Sadiq A.T.	Deep Learning Approaches For Iot Intrusion Detection Systems	Iraqi Journal of Science, 65, 11 (2024)
9646	0.877	Maniriho P.; Niyigaba E.; Bizimana Z.; Twiringiyimana V.; Mahoro L.J.; Ahmad T.	Anomaly-Based Intrusion Detection Approach For Iot Networks Using Machine Learning	CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (2020)
29736	0.874	Balaji S.; Sankaranarayanan S.	Hybrid Deep-Generative Adversarial Network Based Intrusion Detection Model For Internet Of Things Using Binary Particle Swarm Optimization	International Journal of Electrical and Electronics Research, 10, 4 (2022)
2052	0.871	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)
33508	0.871	Saini K.S.; Chaudhary S.	Investigation On Attack Detection In Iot Networks: A Study And Analysis Of The Existing Machine Learning And Deep Learning Techniques	3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025)
23835	0.87	Arabiat A.; Altayeb M.	Enhancing Internet Of Things Security: Evaluating Machine Learning Classifiers For Attack Prediction	International Journal of Electrical and Computer Engineering, 14, 5 (2024)
17808	0.869	Asad U.; Mohammed A.S.	Deep Learning And Industrial Internet Of Things To Improve Smart City Safety	2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 (2023)