A scalable distributed machine learning approach for attack detection in edge computing environments

Abstract The ever-increasing number of IoT applications and cyber–physical services is introducing significant challenges associated to their cyber-security. Due to the constrained nature of the involved devices, some heavier computational tasks, such as deep traffic inspection and classification, essential for implementing automatic attack detection systems, are moved on specialized “edge” devices, in order to distribute the processing intelligence near to the data sources. These edge devices are mainly capable of effectively running pre-built classification models but have not enough storage and processing capabilities to build and upgrade such models from huge volumes of field training data, imposing a serious barrier to the deployment of such solutions. This work leverages the flexibility of cloud-based architectures, together with the recent advancements in the area of large-scale machine learning for shifting the more computationally-expensive and storage-demanding operations to the cloud in order to benefit of edge computing capabilities only for effectively performing traffic classification based on sophisticated Extreme Learning Machines models that are pre-built over the cloud.