The hardware Trojan (HT) has become a major threat for the integrated circuit (IC) industry and supply chain, and has motivated numerous developments of Trojan detection schemes. Although the side-channel method is the most promising one, nearly all of the side-channel methods require fabricated golden chips, which are very difficult to obtain in reality. In this paper, we propose a novel strategy for HT detection using electromagnetic side-channel-based spectrum modeling and analyzing. We utilize the design data at early stage of the IC lifecycle, and the generated spectrum can serve as the golden reference, and thus we do not need the fabricated golden chips anymore. Another very important feature is that our method is immune to the process variation theoretically. Experimental results on selected Advanced Encryption Standard benchmark circuits on FPGA show that our proposed method can effectively detect Trojans even with very small traces.

Previous work has shown that Deep Neural Networks (DNNs), including those currently in use in many fields, are extremely vulnerable to maliciously crafted inputs, known as adversarial examples. Despite extensive and thorough research of adversarial examples in many areas, adversarial 3D data, such as point clouds, remain comparatively unexplored. The study of adversarial 3D data is crucial considering its impact in real-life, high-stakes scenarios including autonomous driving. In this paper, we propose a novel adversarial attack against PointNet++, a deep neural network that performs classification and segmentation tasks using features learned directly from raw 3D points. In comparison to existing works, our attack generates not only adversarial point clouds, but also robust adversarial objects that in turn generate adversarial point clouds when sampled both in simulation and after construction in real world. We also demonstrate that our objects can bypass existing defense mechanisms designed especially against adversarial 3D data.

Cyber-physical systems (CPS) comprise the backbone of national critical infrastructures such as power grids, transportation systems, home automation systems, etc. Because cyber-physical systems are widely used in these applications, the security considerations of these systems should be of very high importance. Compromise of these systems in critical infrastructure will cause catastrophic consequences. In this paper, we will investigate the security vulnerabilities of currently deployed/implemented cyber-physical systems. Our analysis will be from a cross-layer perspective, ranging from full cyber-physical systems to the underlying hardware platforms. In addition, security solutions are introduced to aid the implementation of security countermeasures into cyber-physical systems by manufacturers. Through these solutions, we hope to alter the mindset of considering security as an afterthought in CPS development procedures.

The SAT-based attacks are extremely successful in deobfuscating the traditional combinational logic locking and IC camouflaging schemes. While several SAT-resilient protection schemes that increase the minimum query count of the attack have been proposed recently, none of them satisfy the output corruptibility (error) criteria. Therefore, most of them were combined with high corruptibility schemes to achieve both corruptibility and high query count. These “compound” schemes are successful since existing SAT attacks are agnostic to the corruptibility of the protection scheme. In this paper, we propose an approximate SAT-based attack framework which focuses on the iterative convergence of an attack toward a better solution. This helps our attack reduce a compound scheme to a standalone SAT-resilient scheme. In addition, we relate the problem of minimum query count to a well-known graph problem, and we propose a novel technique to increase the corruptibility of SAT-resilient protection schemes in a controllable manner. This creates protection schemes that have both high query count and corruptibility. Furthermore, due to the approximation resiliency property of these schemes, approximate attacks provide no advantage over exact attacks when attacking them.

As concerns are increasingly raised about data privacy, encrypted database management system (DBMS) based on fully homomorphic encryption (FHE) attracts increasing research attention, as FHE permits DBMS to be directly outsourced to cloud servers without revealing any plaintext data. However, the real-world deployment of FHE-based DBMS faces two main challenges: i) high computational latency, and ii) lack of elastic query processing capability, both of which stem from the inherent limitations of the underlying FHE operators. Here, we introduce HE3DB, a fully homomorphically encrypted, efficient and elastic DBMS framework based on a new FHE infrastructure. By proposing and integrating new arithmetic and logic homomorphic operators, we devise fast and high-precision homomorphic comparison and aggregation algorithms that enable a variety of SQL queries to be applied over FHE ciphertexts, e.g., compound filter-aggregation, sorting, grouping, and joining. In addition, in contrast to existing encrypted DBMS that only support aggregated information retrieval, our framework permits further server-side elastic analytical processing over the queried FHE ciphertexts, such as private decision tree evaluation. In the experiment, we rigorously study the efficiency and flexibility of HE3DB. We show that, compared to the state-of-the-art techniques, HE3DB can homomorphically evaluate end-to-end SQL queries as much as 41X-299X faster than the state-of-the-art solution, completing a TPC-H query over a 16-bit 10K-row database within 241 seconds.