LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing
This work provides an incremental improvement in cyberattack detection for IoT edge computing environments, benefiting network administrators and security professionals by offering a faster and more efficient detection system.
This paper addresses the challenge of cyberattack detection in IoT edge computing by proposing LocKedge, a low-complexity mechanism. It achieves high accuracy, outperforming several machine learning and deep learning methods (NN, CNN, RNN, KNN, SVM, RF, Decision Tree) on the BoT IoT dataset, and also demonstrates lower complexity than NN.
Internet of Things and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the workload of the cloud. We also propose a multi attack detection mechanism called LocKedge Low Complexity Cyberattack Detection in IoT Edge Computing, which has low complexity for deployment at the edge zone while still maintaining high accuracy. LocKedge is implemented in two manners: centralized and federated learning manners in order to verify the performance of the architecture from different perspectives. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT IoT data set. The results show that LocKedge outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.