Cybersecurity Warning System Using Diluted Convolutional Neural Network Framework for IOT Attack Prevention

Abstract

The internet of things (IoT) integrates plans, operations, data management, and strategies, because they continuously support businesses, they could be a new point of entry for cyberattacks. IoT security is being seriously threatened by viruses and illicit downloads. These dangers run the risk of obtaining sensitive information, damaging their reputation and their finances. The attack prevention in IoT is detected in this work using a hybrid optimisation mechanism and deep learning a frame. A cybersecurity warning system (CWS) is proposed in this paper, by first pre-processing the input, then classifying, and finally optimizing it. With the modified particle swarm optimization algorithm (MPSO), the IDS is more effective at identifying both normal and abnormal connection in the networks. The smart initialization phase combines various pre-processing strategies to ensure that the informational features incorporated in the early development phase have been enhanced. Then the dilated convolutional neural network (di-CNN) is used for classification and is optimized by using MPSO algorithm to detect the attack. The recommended method is implemented in MATLAB stimulator. The effectiveness of the proposed CWS method approach has been assessed utilising performance criteria such as accuracy, precision ratio, F1 score, specificity, and detection rate. According to experimental findings, the proposed CWS technique 99% in which is relatively high compared to existing methods of 78%, 84%, and 90% than TCN-IDS, MCNN and DSBEL.