| Title | A novel-cascaded ANFIS-based deep reinforcement learning for the detection of attack in cloud IoT-based smart city applications |
|---|---|
| ID_Doc | 37333 |
| Authors | Almasri, MM; Alajlan, AM |
| Title | A novel-cascaded ANFIS-based deep reinforcement learning for the detection of attack in cloud IoT-based smart city applications |
| Year | 2023 |
| Published | Concurrency And Computation-Practice & Experience, 35.0, 22 |
| 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. |
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