An Effective Intrusion Detection System for Edge Computing Using ConvNeXt and ResNet152V2

Abstract

The proliferation of edge computing, driven by network applications and wireless devices, increases the vulnerability of confidential information to security risks. In this environment, existing intrusion detection algorithms fail to satisfy the requirements of prompt responses, heavy network load management, inadequate extraction of features, and imprecise model classification. In this work, the imbalanced data problem in the input dataset is mitigated using the Data Augmentation Generative Adversarial Network (DAGAN). Next, an efficient ConvNeXt-based feature extraction method is created to retrieve the key characteristics from the dataset for every class. Last, multi-attack intrusion detection is achieved through the deployment of an optimized deep learning classifier based on ResNet152V2. Furthermore, simulation experiments are carried out on the ToN-IoT and BoT-IoT datasets, and the outcomes demonstrate that our suggested model performs better than the existing models, with accuracy levels of 99.20% and 99.31%, respectively. These findings show that this approach is successful in building and refining large-scale IDS in the edge computing framework.