Hybrid VGG19 and 2D-CNN for intrusion detection in the FOG-cloud environment

Fog computing (FC) extends cloud technology by providing services, storage, and network interaction between data centers and end devices. By putting storage and processing closer to the network edge, fog computing may improve IDS effectiveness and efficiency. We develop a cloud-fog intrusion detection system (CFIDS) that employs cloud and fog resources to identify threats in the current research. However, the attack affects data packets from Fog layer nodes. The majority of intrusive cyber-attacks are close variants of previously identified cyber attacks with duplicate data and attributes. In addition, the data packet must be analyzed to determine the intrusion. SYN packets, used to initiate TCP connections, flood a victim's network in a syn threat. To mitigate the impact of syn attacks in a FC environment, it is essential to implement the necessary security measures. Deep learning-based IDS are suitable for protecting fog computing layers from attacks and addressing current issues. The authors of this paper have put forth a new hybrid architecture for DL intrusion detection in fog computing, which combines the use of DL models. Due to the large dimensionality of network data, Radial basis functionbased support vector regression (RBFSVR) is initially used to minimize the dimensionality of the data and reduce the training time. Then, on the cloud server, the integrated VGG19 and 2DCNN are utilized to complete the dataset's training and transfer it to the fog layer, where data transmission is observed and threats are recognized. Experiments with the UNSW-NB15, CICIDS2017, and CICIDS2018 datasets demonstrate that the techniques presented in this paper outperforms other comparable techniques in terms of detection rate, F-score, precision, recall, FAR, and accuracy, thereby solving the problem of intrusion detection. In addition, by analyzing irregular patterns of network traffic, flow duration is utilized to avoid harmful attacks in a fog computing environment.