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Logical obfuscation technique is a mainstream technical means to achieve intellectual property protection and prevent reverse engineering. In this paper, we propose a logical obfuscation attack method based on the gate-level information flow tracking technique, using the information flow analysis method at the gate-level abstraction level, establishing an information flow model, constraining the output and its contamination labels, and solving the obfuscation key sequence satisfying the constraints using an SAT solver. The experimental results show that the attack method has good cracking effect and efficiency for benchmarks generated by five obfuscated encryption algorithms with two test area costs.

Large scale integrate circuits is facing serious threat such as design vulnerabilities, side channels, and hardware Trojans. Traditional functional verification method is difficult to ensure high test coverage, and it is also difficult to detect security vulnerabilities such as side channels and stealthy hardware Trojans. Formal verification methods focus on the equivalence and functional correctness of design, and are difficult to meet security and reliability verification needs. The present work proposes a hardware security and reliability verification method from formal model. The present method can develop formal models for describing the security and reliability behaviour of hardware designs. It can detect potential security vulnerabilities in hardware designs. Experimental results show that the verification method is effective in detecting sensitive information leakage and modification caused by side channels and hardware Trojans.

High-level synthesis (HLS) allows hardware designers to think algorithmically and not worry about low-level, cycle-by-cycle details. This provides the ability to quickly explore the architectural design space and tradeoffs between resource utilization and performance. Unfortunately, security evaluation is not a standard part of the HLS design flow. In this article, we aim to understand the effects of memory-based HLS optimizations on power side-channel leakage. We use Xilinx Vivado HLS to develop different cryptographic cores, implement them on a Spartan-6 FPGA, and collect power traces. We evaluate the designs with respect to resource utilization, performance, and information leakage through power consumption. We have two important observations and contributions. First, the choice of resource optimization directive results in different levels of side-channel vulnerabilities. Second, the partitioning optimization directive can greatly compromise the hardware cryptographic system through power side-channel leakage due to the deployment of memory control logic. We describe an evaluation procedure for power side-channel leakage and use it to make best-effort recommendations about how to design more secure architectures in the cryptographic domain.

Weakly supervised instance segmentation (WSIS) only employs image-level supervision to identify instance class labels and create segmentation masks, drawing increasing attention. Currently, existing WSIS methods primarily focus on activating the most discriminative regions and then inferring the entire instance by analyzing inter-pixel relationships within those regions. However, these identification regions are typically concentrated in limited but critical regions or are mistakenly activated in the background region, making it challenging to address scale variations among instances. Furthermore, different aerial instances often appear in close proximity, resulting in the merging of multiple instances of the same class. To tackle these challenges, we propose a comprehensive approach called Comprehensive Spatial Adaptation Segmentation (CSASeg). Specifically, the self-adaptive spatial-aware enhancement network (SSE) identifies extensive regions by analyzing spatial consistency within the class semantic map. Then, we develop a multi-level projection field (MPF) module to significantly enhance instance-level discrimination through deep-to-shallow residual estimation. Additionally, a foreground enhancement module is incorporated into SSE to reduce background noise while enhancing foreground details, significantly increasing the effectiveness of instance analysis. Extensive experiments conduct on three challenging datasets, iSAID, NWPU VHR-10.v2, and SSDD, demonstrate the competitiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, the development of computer networks has been advancing rapidly, and the world has gradually entered a new era of digital connectivity. While enjoying the convenience brought by digitization, people are also facing increasingly serious threats from network security (NS) issues. Due to the significant shortcomings in accuracy and efficiency of traditional Long Short-Term Memory (LSTM) neural networks (NN), different scholars have conducted research on computer NS situation prediction methods to address the aforementioned issues of traditional LSTM based NS situation prediction algorithms. Although these algorithms can improve the accuracy of NS situation prediction to a certain extent, there are still some limitations, such as low computational efficiency, low accuracy, and high model complexity. To address these issues, new methods and techniques have been proposed, such as using NN and machine learning techniques to improve the accuracy and efficiency of prediction models. This article referred to the Bidirectional Gated Recurrent Unit (BiGRU) improved by Gated Recurrent Unit (GRU), and introduced a multi model NS situation prediction algorithm with attention mechanism. In addition, the improved Particle Swarm Optimization (PSO) algorithm can be utilized to optimize hyperparameters and improve the training efficiency of the GRU NN. The experimental results on the UNSW-NB15 dataset show that the algorithm had an average absolute error of 0.0843 in terms of NS prediction accuracy. The RMSE was 0.0932, which was lower than traditional prediction algorithms LSTM and GRU, and significantly improved prediction accuracy. [ABSTRACT FROM AUTHOR]

Hardware Trojan detection is a very difficult challenge. However, the combination of symbolic execution and metamorphic testing is useful for detecting hardware Trojans in Verilog code. In this paper, symbolic execution and metamorphic testing were combined to detect internal conditionally triggered hardware Trojans in the register-transfer level design. First, control flow graphs of Verilog code were generated. Next, parallel symbolic execution and satisfiability modulo theories solver generated test patterns. Finally, metamorphic testing detected the hardware Trojans. The work used Trust-Hub benchmarks in experiments.

Cryptographic cores are known to leak information about their private key due to runtime variations, and there are many well-known attacks that can exploit this timing channel. In this paper, we study how information theoretic measures can quantify the amount of key leakage that can be exacted from runtime measurements. We develop and analyze 22 Rivest-Shamir-Adleman (RSA) hardware designs - each with unique performance optimizations, timing channel mitigation techniques, or discretization/randomization countermeasures. We demonstrate the effectiveness of information theoretic measures for quantifying timing leakage through correlation analysis of information theoretic measurements and attack results. Experimental results show that mutual information is a promising technique for quantifying timing leakage for RSA, advanced encryption standard, and elliptic curve cryptography ciphers, i.e., the mutual information correlates to being able to successfully guess the value of the private key. This is an important step toward a hardware security metric which allows designers to reason about security alongside traditional hardware design metrics like area, performance, and power.

High-assurance systems found in safety-critical infrastructures are facing steadily increasing cyber threats. These critical systems require rigorous guarantees in information flow security to prevent confidential information from leaking to an unclassified domain and the root of trust from being violated by an untrusted party. To enforce bit-tight information flow control, gate-level information flow tracking (GLIFT) has recently been proposed to precisely measure and manage all digital information flows in the underlying hardware, including implicit flows through hardware-specific timing channels. However, existing work in this realm either restricts to two-level security labels or essentially targets two-input primitive gates and several simple multilevel security lattices. This article provides a general way to expand the GLIFT method for multilevel security. Specifically, it formalizes tracking logic for an arbitrary Boolean gate under finite security lattices, presents a precise tracking logic generation method for eliminating false positives in GLIFT logic created in a constructive manner, and illustrates application scenarios of GLIFT for enforcing multilevel information flow security. Experimental results show various trade-offs in precision and performance of GLIFT logic created using different methods. It also reveals the area and performance overheads that should be expected when expanding GLIFT for multilevel security.