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Untrusted foundries pose threats of integrated circuit (IC) piracy and counterfeiting, and this has motivated research into logic locking. Strong logic locking approaches potentially prevent piracy and counterfeiting by preventing unauthorized replication and use of ICs. Unfortunately, recent work has shown that most state-of-the-art logic locking techniques are vulnerable to attacks that utilize Boolean Satisfiability (SAT) solvers. In this article, we extend our prior work on using silicon nanowire (SiNW) field-effect transistors (FETs) to produce obfuscated ICs that are resistant to reverse engineering attacks, such as the sensitization attack, SAT and approximate SAT attacks, as well as tracked signal attacks. Our method is based on exchanging some logic gates in the original design with a set of polymorphic gates (PLGs), designed using SiNW FETs, and augmenting the circuit with a small block, whose output is untraceable, namely, URSAT. The URSAT may not offer very strong resilience against the combined AppSAT-removal attack. Strong URSAT is achieved using only CMOS-logic gates, namely, S-URSAT. The proposed technique, S-URSAT + PLG-based traditional encryption, designed using SiNW FETs, increases the security level of the design to robustly thwart all existing attacks, including combined AppSAT-removal attack, with small penalties. Then, we evaluate the effectiveness of our proposed methods and subject it to a thorough security analysis. We also evaluate the performance penalty of the technique and find that it results in very small overheads in comparison to other works. The average area, power, and delay overheads of implementing 64 baseline key-bits of S-URSAT for small benchmarks are 5.03%, 2.60%, and −2.26%, respectively, while for large benchmarks they are 2.37%, 1.18%, and −1.93%.

Regardless of revolutionizing improvements in various domains thanks to recent advancements in the field of Deep Learning (DL), recent studies have demonstrated that DL networks are susceptible to adversarial attacks. Such attacks are crucial in sensitive environments to make critical and life-changing decisions, such as health decision-making. Research efforts on using textual adversaries to attack DL for natural language processing (NLP) have received increasing attention in recent years. Among the available textual adversarial studies, Electronic Health Records (EHR) have gained the least attention. This paper investigates the effectiveness of adversarial attacks on clinical document classification and proposes a defense mechanism to develop a robust convolutional neural network (CNN) model and counteract these attacks. Specifically, we apply various black-box attacks based on concatenation and editing adversaries on unstructured clinical text. Then, we propose a defense technique based on feature selection and filtering to improve the robustness of the models. Experimental results show that a small perturbation to the unstructured text in clinical documents causes a significant drop in performance. Performing the proposed defense mechanism under the same adversarial attacks, on the other hand, avoids such a drop in performance. Therefore, it enhances the robustness of the CNN model for clinical document classification.

In this study, we developed a complete flow for the design of monolithic 3D ICs. We have taken the register-transfer level netlist of a circuit as the input and synthesized it to construct the gate-level netlist. Next, we partitioned the circuit using custom-made partitioning algorithms and implemented the place and route flow of the entire 3D IC by repurposing 2D electronic design automation tools. We implemented two different partitioning algorithms, namely the min-cut and the analytical quadratic (AQ) algorithms, to assign the cells in different tiers. We applied our flow on three different benchmark circuits and compared the total power dissipation of the 3D designs with their 2D counterparts. We also compared our results with that of similar works and obtained significantly better performance. Our two-tier 3D flow with AQ partitioner obtained 37.69%, 35.06%, and 12.15% power reduction compared to its 2D counterparts on the advanced encryption standard, floating-point unit, and fast Fourier transform benchmark circuits, respectively. Finally, we analyzed the type of circuits that are more applicable for a 3D layout and the impact of increasing the number of tiers of the 3D design on total power dissipation.

A strong logic locking approach could be utilized to contest serious threats on ICs. However, recent Boolean satisfiability (SAT) attack successfully decrypts all existing logic locking techniques. Even though different methods have been used to make the attack execution time increase exponentially, those methods are vulnerable to tracked and removed resilient SAT structure based attacks. In this work, we use silicon nanowire FETs to produce an obfuscated IC against reverse engineering attacks and reduce the performance penalty by exchanging some logic gates with different polymorphic gates based SiNW FETs and incorporating a small block of circuitry whose output is untraceable.

Due to the globalization of Integrated Circuit (IC) design in the semiconductor industry and the outsourcing of chip manufacturing, third Party Intellectual Properties (3PIPs) become vulnerable to IP piracy, reverse engineering, counterfeit IC, and hardware trojans. A designer has to employ a strong technique to thwart such attacks, e.g. using Strong Logic Locking method [1]. But, such technique cannot be used to protect some circuits since the inserted key-gates rely on the topology of the circuit. Also, it requires higher power, delay, and area overheads compared to other techniques. In this paper, we present the use of spintronic devices to help protect ICs with less performance overhead. We then evaluate the proposed design based on security metric and performance overhead. One of the best spintronic device candidates is the All Spin Logic due to its unique properties: small area, no spin-charge signal conversion, and its compatibility with conventional CMOS technology.

Due to the outsourcing of chip manufacturing, countermeasures against Integrated Circuit (IC) piracy, reverse engineering, IC overbuilding and hardware Trojans (HTs) become a hot research topic. To protect an IC from these attacks, logic encryption techniques have been considered as a low-cost defense mechanism. In this paper, our proposal is to insert the multiplexer (MUX) with two cases: (i) we randomly insert MUXs equal to half of the output bit number (half MUX insertions); and (ii) we insert MUXs equal to the number of output bits (full MUX insertions). Hamming distance is adopted as a security evaluation. We also measure the delay, power and area overheads with the proposed technique.

The outsourcing of integrated circuit (IC) fabrication services to overseas manufacturing foundry has raised security and privacy concerns with regard to intellectual property (IP) protection as well as the integrity maintenance of the fabricated chips. One way to protect ICs from malicious attacks is to encrypt and obfuscate the IP design by incorporating additional key gates, namely logic encryption or logic locking. The state-of-the-art logic encryption techniques certainly incur considerable performance overhead upon the genuine IP design. The focus of this paper is to leverage the unique property of emerging transistor technology on reducing the performance overhead as well as preserving the robustness of logic locking technique. We design the polymorphic logic gate using silicon nanowire field effect transistors (SiNW FETs) to replace the conventional Exclusive-OR (XOR)-based logic cone. We then evaluate the proposed technique based on security metric and performance overhead.

In this review article for Internet of Things (IoT) applications, important low-power design techniques for digital and mixed-signal analog–digital converter (ADC) circuits are presented. Emerging low voltage logic devices and non-volatile memories (NVMs) beyond CMOS are illustrated. In addition, energy-constrained hardware security issues are reviewed. Specifically, light-weight encryption-based correlational power analysis, successive approximation register (SAR) ADC security using tunnel field effect transistors (FETs), logic obfuscation using silicon nanowire FETs, and all-spin logic devices are highlighted. Furthermore, a novel ultra-low power design using bio-inspired neuromorphic computing and spiking neural network security are discussed.