The deep learning-based profiling attacks have received significant attention for their potential against masking-protected devices. Currently, additional capabilities like exploiting only a segment of the side-channel traces or having knowledge of the specific countermeasure scheme have been granted to attackers during the profiling phase. In case either capability is removed, a practical profiling attack faces great difficulty and complexity.

Recently, stacked intelligent metasurfaces (SIMs) have aroused widespread discussions as an innovative technology for directly processing electromagnetic (EM) wave signals. By stacking multiple programmable metasurface layers, an SIM has the ability to provide additional spatial degrees of freedom without the introduction of expensive radio-frequency chains, which may outperform reconfigurable intelligent surfaces (RISs) with single-layer structures.

Aggregating metadata in the ciphertext field is an attractive property brought by homomorphic encryption (HE) for privacy-sensitive computing tasks, therefore, research on the next-generation wireless networks has treated it as one of the promising cryptographic techniques for various scenarios. However, existing schemes are far from being deployed in various computing scenarios due to their high computational complexity and ciphertext expansion, especially for bandwidth-limited and latency-sensitive wireless scenarios.

Modern integrated circuits (ICs) require a complex, outsourced supply-chain, involving computer-aided design (CAD) tools, expert knowledge, and advanced foundries. This complexity has led to various security threats, such as Trojans inserted by adversaries during outsourcing, but also run-time threats like physical probing. Our proposed design-time solution, DEFense , is an extensible CAD framework for holistic assessment and proactive mitigation of multiple prominent threats.

Website Fingerprinting (WF) is a network traffic mining technique for anonymous traffic identification, which enables a local adversary to identify the target website that an anonymous network user is browsing. WF attacks based on deep convolutional neural networks (CNN) get the state-of-the-art anonymous traffic classification performance. However, due to the locality restriction of CNN architecture for feature extraction on sequence data, these methods ignore the temporal feature extraction in the anonymous traffic analysis.