Smart City Gnosys
Smart city article details
| Title | A Novel-Cascaded Anfis-Based Deep Reinforcement Learning For The Detection Of Attack In Cloud Iot-Based Smart City Applications |
|---|---|
| ID_Doc | 3587 |
| Authors | Almasri M.M.; Alajlan A.M. |
| Year | 2023 |
| Published | Concurrency and Computation: Practice and Experience, 35, 22 |
| DOI | http://dx.doi.org/10.1002/cpe.7738 |
| Abstract | Vast usages of Internet of Things (IoT) devices in various smart applications have laid a foundation for the evolution of modern smart cities. The increasing dependency of smart city applications on communication and information technologies enhances operational efficiency, sustainability, and automation of city services. However, due to the heterogeneous nature of IoT devices, the network faces critical security issues while executing continued network operations and services, particularly by cyber-attacks. One of the predominant and rampant cyber-attacks in smart city applications is botnet attacks. Therefore, a novel deep learning model for the detection and isolation of cyber-attacks is proposed in the cloud IoT-based smart city applications to protect against such cyber-attacks. The proposed framework utilizes two different modules to automatically detect and isolate the malicious traffic emanating from compromised IoT devices with more efficiency. Here, two different datasets namely the IoT network intrusion and the ISCX 2012 IDs datasets are utilized for the evaluation of the proposed framework. In the first phase, the compromised device which communicates malicious network traffics through the network is identified using a cascaded adaptive neuro-fuzzy inference system (CANFIS). After detection, IP address of abnormal traffic is recorded and informed to the system administrator. In the second phase, communication pathways of compromised devices with other normal devices are blocked and the compromised devices are isolated from the network using the modified deep reinforcement learning (MDRL) approach. The analytic result shows that the proposed framework achieves a greater accuracy rate of about 98.7% as compared to other state-of-art methods. © 2023 John Wiley & Sons, Ltd. |
| Author Keywords | cascaded adaptive neuro-fuzzy inference system; cloud; cyber-attack; internet of things; modified deep reinforcement learning; smart city |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
4601  | 0.889 | Kumar P.J.; Neduncheliyan S. | A Shark Inspired Ensemble Deep Learning Stacks For Ensuring The Security In Internet Of Things (Iot)-Based Smart City Infrastructure | International Journal of Computational Intelligence Systems, 17, 1 (2024) |
| 29764 | 0.885 | Bose S.; Maheswaran N.; Gokulraj G.; Anitha T.; Shruthi T.; Vijayaraj G. | Hybrid Intrusion Detection System For Iot Against Adversarial Threats Using Intelligent Rdls Model | Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024 (2024) |
| 1446 | 0.882 | 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) |
| 7014 | 0.881 | Reis M.J.C.S. | Ai-Driven Anomaly Detection For Securing Iot Devices In 5G-Enabled Smart Cities | Electronics (Switzerland), 14, 12 (2025) |
| 3350 | 0.878 | Sharma V.; Seetharaman T.; Mohammed Essam K.; Alkhayyat A. | A Novel Explainable Artificial Intelligence-Based Deep Reinforcement Learning For Secured Smart City Applications | Lecture Notes in Networks and Systems, 787 LNNS (2023) |
| 47782 | 0.877 | Ahmed Y.; Beyioku K.; Yousefi M. | Securing Smart Cities Through Machine Learning: A Honeypot-Driven Approach To Attack Detection In Internet Of Things Ecosystems | IET Smart Cities, 6, 3 (2024) |
| 3390 | 0.877 | Gupta B.B.; Chui K.T.; Gaurav A.; Arya V.; Chaurasia P. | A Novel Hybrid Convolutional Neural Network- And Gated Recurrent Unit-Based Paradigm For Iot Network Traffic Attack Detection In Smart Cities | Sensors (Basel, Switzerland), 23, 21 (2023) |
| 33508 | 0.875 | 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) |
| 38988 | 0.875 | Sriram S.; Vinayakumar R.; Alazab M.; Soman K.P. | Network Flow Based Iot Botnet Attack Detection Using Deep Learning | IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2020 (2020) |
| 17981 | 0.873 | Himdi T.; Ishaque M. | Deep Learning-Enhanced Anomaly Detection For Iot Security In Smart Cities | ARPN Journal of Engineering and Applied Sciences, 19, 6 (2024) |