  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.
