Abstract

The fast growth of Internet of Things (IoT) gadgets and 5G networks has increased linkage and accessibility. However, growing interconnectivity poses new threat levels in these environments, making intrusion detection critical. In this article, we introduce a novel security framework that centres on deep learning and is tailored to support the particular risks posed by IoT channels and 5G networks. Deep neural networks are used in our proposed framework to effectively analyze network activity patterns and recognize any possible breaches in real-time communication. Deep learning autonomously learns complicated features and patterns, facilitating the proposed model's adaptability to changing threat vectors and traffic features. This research proposed a Hybrid model using enhanced light-weight CNNs architecture (MobileNetV3-SVM) and Transfer learning (TL) for intrusion detection in 5G communication. The proposed model utilizes the advantages of a multi-layered structure, which enables it to acquire knowledge from raw network information hierarchically. It provides the ability to distinguish between authentic and malicious behaviour efficiently. We have implemented several cutting-edge strategies to maximize the effectiveness of intrusion detection in environments characterized by limited availability of resources, such as those associated with the IoT and high-speed 5G networks. The proposed hybrid model processes network packets in real-time using light-weight MobileNet, reducing the computational overhead and making it suitable for IoT and 5G edge devices. In the proposed model, a MobileNetV3-SVM auto-classifies the network's intrusion images, enhancing the overall accuracy. In addition, to address the issue of limited labelled data in dynamic and constantly changing systems, we use a transfer learning strategy to deal with this issue. The proposed hybrid model and existing CNN- architectures, i.e., VGG-16, VGG-19, Efficient-Net and Inception-Net, are tested on CICIDS-2017, 2018 and UNSW-NB15 IoT 5G security datasets. During the experimental assessment, we demonstrated the strength of the proposed model by simulating a wide range of network settings and intrusion scenarios. Experimental findings show considerable improvements by the proposed hybrid model in accuracy, precision, false positive rates, Matthew's Correlation Coefficient (MCC) and AUC-ROC over existing approaches.