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Side-channel attacks leverage correlations between power consumption and intermediate encryption results to infer encryption keys. Recent studies show that deep learning offers promising results in the context of side-channel attacks. However, neural networks utilized in deep-learning side-channel attacks are complex with a substantial number of parameters and consume significant memory. As a result, it is challenging to perform deep-learning side-channel attacks on resource-constrained devices. In this paper, we propose a framework, TinyPower, which leverages pruning to reduce the number of neural network parameters for side-channel attacks. Pruned neural networks obtained from our framework can successfully run side-channel attacks with significantly fewer parameters and less memory. Specifically, we focus on structured pruning over filters of Convolutional Neural Networks (CNNs). We demonstrate the effectiveness of structured pruning over power and EM traces of AES-128 running on microcontrollers (AVR XMEGA and ARM STM32) and FPGAs (Xilinx Artix-7). Our experimental results show that we can achieve a reduction rate of 98.8% (e.g., reducing the number of parameters from 53.1 million to 0.59 million) on a CNN and still recover keys on XMEGA. For STM32 and Artix-7, we achieve a reduction rate of 92.9% and 87.3% on a CNN respectively. We also demonstrate that our pruned CNNs can effectively perform the attack phase of side-channel attacks on a Raspberry Pi 4 with less than 2.5 millisecond inference time per trace and less than 41 MB memory usage per CNN. 

Creativity focuses on the generation of novel and useful ideas. In this paper, we propose an approach to automatically generating creative requirements candidates via the adversarial examples resulted from applying small changes (perturbations) to the original requirements descriptions. We present an architecture where the perturbator and the classifier positively influence each other. Meanwhile, we ensure that each adversarial example is uniquely traceable to an existing feature of the software, instrumenting explainability. Our experimental evaluation of six datasets shows that around 20% adversarial shift rate is achievable. In addition, a human subject study demonstrates our results are more clear, novel, and useful than the requirements candidates outputted from a state-of-the-art machine learning method. To connect the creative requirements closer with software development, we collaborate with a software development team and show how our results can support behavior-driven development for a web app built by the team.