Edge enhanced deep learning system for IoT edge device security analytics

The processing of locally harvested data at the physically accessible edge devices opens a new avenue of security threats for edge enhanced analytics. Cryptographic algorithms are used to secure the data being processed on the edge device. However, the implementation weakness of the algorithms on the edge devices can lead to side-channel attack vulnerability, which is exacerbated with the application of machine-learning techniques. This research proposes a deep learning-based system integrated at the edge device to identify the side-channel leakages. To design such a deep learning-based system, one of the challenges is formulating the suitable attack model for the underlying target algorithm. Based on the previous findings, three machine learning-based side-channel attack models are curated and investigated for the edge device security evaluations. As a test case, the standard elliptic-curve cryptographic algorithm is selected. Moreover, quantitative analysis is provided for the best attack model selection using standard machine-learning evaluation metrics. A comparative analysis is performed on the raw unaligned data samples and reduced feature-engineered samples using edge enhanced security analytics. The investigation concludes that the vulnerable algorithm implementation can lead to the secret key recovery from the edge device, with 96% accuracy, using a neural-network-based algorithm to analyse side-channel attacks.