Abstract
Internet of Things (IoT) 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 Cloud’s workload. 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 manner and federated learning manner 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.
Highlights
LocKedge is implemented in two manners: centralized and federated learning manners in order to verify the performance of the architecture from different perspectives
The results show that LocKedge outperforms other algorithms such as Neural Network (NN), Convolutional Neural Network (CNN), RNN, K-nearest neighbors (KNN), Support Vector Machine (SVM), KNN, Random Forest (RF) and Decision Tree in terms of accuracy and NN in terms of complexity
We investigate LocKedge in two manners: centralized learning and federated learning manner to train the system for attack detection
Summary
Offloading computationally intensive tasks to a cloud center may result in a delay, due to the time needed to transmit, process, and receive a large amount of data To overcome this limitation, edge computing was born to quickly perform the necessary computational task in the network edge. LocKedge with its high accuracy and low complexity can be suitable for deployment at edge devices with limited computational capacity. On another hand, we investigate LocKedge in two manners: centralized learning and federated learning manner to train the system for attack detection.
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