Deep hybrid model for attack detection in IoT-fog architecture with improved feature set and optimal training

Abstract

IoT-Fog computing provides a wide range of services for end-based IoT systems. End IoT devices interface with cloud nodes and fog nodes to manage client tasks. Critical attacks like DDoS and other security risks are more likely to compromise IoT end devices while they are collecting data between the fog and the cloud layer. It’s important to find these network vulnerabilities early. By extracting features and placing the danger in the network, DL is crucial in predicting end-user behavior. However, deep learning cannot be carried out on Internet of Things devices because to their constrained calculation and storage capabilities. In this research, we suggest a three-stage Deep Hybrid Detection Model for Attack Detection in IoT-Fog Architecture. Improved Z-score normalization-based data preparation will be carried out in the initial step. On the basis of preprocessed data, features like IG, raw data, entropy, and enhanced MI are extracted in the second step. The collected characteristics are used as input to hybrid classifiers dubbed optimized Deep Maxout and Deep Belief Network (DBN) in the third step of the process to classify the assaults based on the input dataset. A hybrid optimization model called the BMUJFO (Blue Monkey Updated Jellyfish Optimization) technique is presented for the best Deep Maxout training. Additionally, the suggested model produced higher accuracy, precision, sensitivity, and specificity results, with values of 95.26 percent, 94.84%, 96.28%, and 97.84%, respectively.