Exploring Edge TPU for Network Intrusion Detection in IoT
It addresses computational and energy resource challenges for IoT edge security, but is incremental as it builds on existing machine learning approaches.
This paper explored using Google's Edge TPU for deep learning-based network intrusion detection in IoT, focusing on computational and energy efficiency, and found that an ARM Cortex A53 CPU outperformed the Edge TPU for small model sizes.
This paper explores Google's Edge TPU for implementing a practical network intrusion detection system (NIDS) at the edge of IoT, based on a deep learning approach. While there are a significant number of related works that explore machine learning based NIDS for the IoT edge, they generally do not consider the issue of the required computational and energy resources. The focus of this paper is the exploration of deep learning-based NIDS at the edge of IoT, and in particular the computational and energy efficiency. In particular, the paper studies Google's Edge TPU as a hardware platform, and considers the following three key metrics: computation (inference) time, energy efficiency and the traffic classification performance. Various scaled model sizes of two major deep neural network architectures are used to investigate these three metrics. The performance of the Edge TPU-based implementation is compared with that of an energy efficient embedded CPU (ARM Cortex A53). Our experimental evaluation shows some unexpected results, such as the fact that the CPU significantly outperforms the Edge TPU for small model sizes.