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Found 9 papers, 0 papers with code
A Review and Comparison of AI Enhanced Side Channel Analysis
In this work, we will examine the latest state-of-the-art deep learning techniques for side channel analysis, the theory behind them, and how they are conducted.
Hardware Phi-1.5B: A Large Language Model Encodes Hardware Domain Specific Knowledge
The creation of this first pretrained, hardware domain specific large language model marks a significant advancement, offering improved performance in hardware design and verification tasks and illustrating a promising path forward for AI applications in the semiconductor sector.
Exploring Adversarial Examples for Efficient Active Learning in Machine Learning Classifiers
As a result, our theoretical proofs provide support to more efficient active learning methods with the help of adversarial examples, contrary to previous works where adversarial examples are often used as destructive solutions.
CHIMERA: A Hybrid Estimation Approach to Limit the Effects of False Data Injection Attacks
Our simulation experiments based on the load data from New York state demonstrate that CHIMERA can effectively mitigate 91. 74% of the cases in which FDIAs can maliciously modify the contingencies.
IoT Security: An End-to-End View and Case Study
The work in this paper raises the alarm again for the IoT device manufacturers to better secure their products in order to prevent malware attacks like Mirai.
Cryptography and Security
MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation
Modern deep learning enabled artificial neural networks, such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN), have achieved a series of breaking records on a broad spectrum of recognition applications.
PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning
One of the most exciting advancements in AI over the last decade is the wide adoption of ANNs, such as DNN and CNN, in many real-world applications.
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
In this work, we for the first time investigate the multi-factor adversarial attack problem in practical model optimized deep learning systems by jointly considering the DNN model-reshaping (e. g. HashNet based deep compression) and the input perturbations.
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications
This paper presents a downscaling attack that targets the data scaling process in deep learning applications.
