Attack detection and secure iot using adaptive neuro fuzzy inference scheme

Abstract

Nowadays, the wide adoption of the modern Internet of Things (IoT) paradigm has brought about the tremendous development of smart cities. Smart cities operate in real-time world to promote ease and also the human life quality with regard to efficiency and comfort. A security concern along with privacy is considered as a foremost issue in several smart cities. The security vulnerability in IoT-centered systems creates security threats which affect smart surroundings applications. Therefore, there is basically a requirement for Intrusion Detection Systems (IDS) for mitigating the IoT-related security outbreaks which took the entire benefits of security liabilities. In existing works, the accuracy in the procedure of detection and security are the main challenge. To trounce these drawbacks, in the first stage of the work the IDS are proposed intended for the identification of attacks in IoT of city depending on the Deep Learning Modified Neural Networks (DLMNN) classification approach. Initially, the values of sensor from smart cities are provided to the training phase of IDS system which is then employed for the purpose of testing values. Then, the step of preprocessing is carried out, followed by feature selection using entropy-Hummingbird Optimization Algorithm (HOA). The classified outcomes are analyzed afterwards and is employed for the prediction of outcome. Then, the task of secured data sharing is carried with the use of Krill Heard Advanced Encryption Standard (KH-AES) approach. Finally, the resultant outcome is forecasted. Experimental result of the suggested method employed in classification, feature selection, and secured data sharing are then compared with the traditional methods. newline