Side Channel

Side-channel analysis exploits unintended information leakage from a system's physical implementation (e.g., power consumption, timing, electromagnetic emissions, acoustics) to infer sensitive data, such as cryptographic keys or model parameters. Current research focuses on developing sophisticated attacks leveraging machine learning, particularly deep learning models like convolutional neural networks and generative adversarial networks, to analyze these side channels and improve attack effectiveness against various targets, including cryptographic hardware, neural network accelerators, and even AI assistants. This field is crucial for enhancing the security of embedded systems and cloud services, as understanding and mitigating these vulnerabilities is vital for protecting sensitive information and maintaining system integrity.