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Kuo, Man-Hsuan, Hu, Chun-Ming, Lee, Kuen-Jong.  2019.  Time-Related Hardware Trojan Attacks on Processor Cores. 2019 IEEE International Test Conference in Asia (ITC-Asia). :43–48.

Real-time clock circuits are widely used in modern electronic systems to provide time information to the systems at the beginning of the system power-on. In this paper, we present two types of Hardware Trojan designs that employ the time information as the trigger conditions. One is a real-time based Trojan, which will attack a system at some specific realworld time. The other is a relative-time based Trojan, which will be triggered when a specific time period passes after the system is powered on. In either case when a Trojan is triggered its payload may corrupt the system or leakage internal information to the outside world. Experimental results show that the extra power consumption, area overhead and delay time are all quite small and thus the detection of the Trojans is difficult by using traditional side-channel detection methods.

Guo, Xiaolong, Zhu, Huifeng, Jin, Yier, Zhang, Xuan.  2019.  When Capacitors Attack: Formal Method Driven Design and Detection of Charge-Domain Trojans. 2019 Design, Automation Test in Europe Conference Exhibition (DATE). :1727–1732.

The rapid growth and globalization of the integrated circuit (IC) industry put the threat of hardware Trojans (HTs) front and center among all security concerns in the IC supply chain. Current Trojan detection approaches always assume HTs are composed of digital circuits. However, recent demonstrations of analog attacks, such as A2 and Rowhammer, invalidate the digital assumption in previous HT detection or testing methods. At the system level, attackers can utilize the analog properties of the underlying circuits such as charge-sharing and capacitive coupling effects to create information leakage paths. These new capacitor-based vulnerabilities are rarely covered in digital testings. To address these stealthy yet harmful threats, we identify a large class of such capacitor-enabled attacks and define them as charge-domain Trojans. We are able to abstract the detailed charge-domain models for these Trojans and expose the circuit-level properties that critically contribute to their information leakage paths. Aided by the abstract models, an information flow tracking (IFT) based solution is developed to detect charge-domain leakage paths and then identify the charge-domain Trojans/vulnerabilities. Our proposed method is validated on an experimental RISC microcontroller design injected with different variants of charge-domain Trojans. We demonstrate that successful detection can be accomplished with an automatic tool which realizes the IFT-based solution.

Shi, Qihang, Vashistha, Nidish, Lu, Hangwei, Shen, Haoting, Tehranipoor, Bahar, Woodard, Damon L, Asadizanjani, Navid.  2019.  Golden Gates: A New Hybrid Approach for Rapid Hardware Trojan Detection Using Testing and Imaging. 2019 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :61–71.

Hardware Trojans are malicious modifications on integrated circuits (IC), which pose a grave threat to the security of modern military and commercial systems. Existing methods of detecting hardware Trojans are plagued by the inability of detecting all Trojans, reliance on golden chip that might not be available, high time cost, and low accuracy. In this paper, we present Golden Gates, a novel detection method designed to achieve a comparable level of accuracy to full reverse engineering, yet paying only a fraction of its cost in time. The proposed method inserts golden gate circuits (GGC) to achieve superlative accuracy in the classification of all existing gate footprints using rapid scanning electron microscopy (SEM) and backside ultra thinning. Possible attacks against GGC as well as malicious modifications on interconnect layers are discussed and addressed with secure built-in exhaustive test infrastructure. Evaluation with real SEM images demonstrate high classification accuracy and resistance to attacks of the proposed technique.

Nejat, Arash, Kazemi, Zahra, Beroulle, Vincent, Hely, David, Fazeli, Mahdi.  2019.  Restricting Switching Activity Using Logic Locking to Improve Power Analysis-Based Trojan Detection. 2019 IEEE 4th International Verification and Security Workshop (IVSW). :49–54.

Nowadays due to economic reasons most of the semiconductor companies prefer to outsource the manufacturing part of their designs to third fabrication foundries, the so-called fabs. Untrustworthy fabs can extract circuit blocks, the called intellectual properties (IPs), from the layouts and then pirate them. Such fabs are suspected of hardware Trojan (HT) threat in which malicious circuits are added to the layouts for sabotage objectives. HTs lead up to increase power consumption in HT-infected circuits. However, due to process variations, the power of HTs including few gates in million-gate circuits is not detectable in power consumption analysis (PCA). Thus, such circuits should be considered as a collection of small sub-circuits, and PCA must be individually performed for each one of them. In this article, we introduce an approach facilitating PCA-based HT detection methods. Concerning this approach, we propose a new logic locking method and algorithm. Logic locking methods and algorithm are usually employed against IP piracy. They modify circuits such that they do not correctly work without applying a correct key to. Our experiments at the gate level and post-synthesis show that the proposed locking method and algorithm increase the proportion of HT activity and consequently HT power to circuit power.

Wang, Yuze, Han, Tao, Han, Xiaoxia, Liu, Peng.  2019.  Ensemble-Learning-Based Hardware Trojans Detection Method by Detecting the Trigger Nets. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). :1–5.

With the globalization of integrated circuit (IC) design and manufacturing, malicious third-party vendors can easily insert hardware Trojans into their intellect property (IP) cores during IC design phase, threatening the security of IC systems. It is strongly required to develop hardware-Trojan detection methods especially for the IC design phase. As the particularity of Trigger nets in Trojan circuits, in this paper, we propose an ensemble-learning-based hardware-Trojan detection method by detecting the Trigger nets at the gate level. We extract the Trigger-net features for each net from known netlists and use the ensemble learning method to train two detection models according to the Trojan types. The detection models are used to identify suspicious Trigger nets in an unknown detected netlist and give results of suspiciousness values for each detected net. By flagging the top n% suspicious nets of each detection model as the suspicious Trigger nets based on the suspiciousness values, the proposed method can achieve, on average, 88% true positive rate, 90% true negative rate, and 90% Accuracy.

Inaba, Koutaro, Yoneda, Tomohiro, Kanamoto, Toshiki, Kurokawa, Atsushi, Imai, Masashi.  2019.  Hardware Trojan Insertion and Detection in Asynchronous Circuits. 2019 25th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC). :134–143.

Hardware Trojan threats caused by malicious designers and untrusted manufacturers have become one of serious issues in modern VLSI systems. In this paper, we show some experimental results to insert hardware Trojans into asynchronous circuits. As a result, the overhead of hardware Trojan insertion in asynchronous circuits may be small for malicious designers who have enough knowledge about the asynchronous circuits. In addition, we also show several Trojan detection methods using deep learning schemes which have been proposed to detect synchronous hardware Trojan in the netlist level. We apply them to asynchronous hardware Trojan circuits and show their results. They have a great potential to detect a hardware Trojan in asynchronous circuits.

Danger, Jean-Luc, Fribourg, Laurent, Kühne, Ulrich, Naceur, Maha.  2019.  LAOCOÖN: A Run-Time Monitoring and Verification Approach for Hardware Trojan Detection. 2019 22nd Euromicro Conference on Digital System Design (DSD). :269–276.

Hardware Trojan Horses and active fault attacks are a threat to the safety and security of electronic systems. By such manipulations, an attacker can extract sensitive information or disturb the functionality of a device. Therefore, several protections against malicious inclusions have been devised in recent years. A prominent technique to detect abnormal behavior in the field is run-time verification. It relies on dedicated monitoring circuits and on verification rules generated from a set of temporal properties. An important question when dealing with such protections is the effectiveness of the protection against unknown attacks. In this paper, we present a methodology based on automatic generation of monitoring and formal verification techniques that can be used to validate and analyze the quality of a set of temporal properties when used as protection against generic attackers of variable strengths.

Han, Tao, Wang, Yuze, Liu, Peng.  2019.  Hardware Trojans Detection at Register Transfer Level Based on Machine Learning. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). :1–5.

To accurately detect Hardware Trojans in integrated circuits design process, a machine-learning-based detection method at the register transfer level (RTL) is proposed. In this method, circuit features are extracted from the RTL source codes and a training database is built using circuits in a Hardware Trojans library. The training database is used to train an efficient detection model based on the gradient boosting algorithm. In order to expand the Hardware Trojans library for detecting new types of Hardware Trojans and update the detection model in time, a server-client mechanism is used. The proposed method can achieve 100% true positive rate and 89% true negative rate, on average, based on the benchmark from Trust-Hub.

Gountia, Debasis, Roy, Sudip.  2019.  Checkpoints Assignment on Cyber-Physical Digital Microfluidic Biochips for Early Detection of Hardware Trojans. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). :16–21.

Present security study involving analysis of manipulation of individual droplets of samples and reagents by digital microfluidic biochip has remarked that the biochip design flow is vulnerable to piracy attacks, hardware Trojans attacks, overproduction, Denial-of-Service attacks, and counterfeiting. Attackers can introduce bioprotocol manipulation attacks against biochips used for medical diagnosis, biochemical analysis, and frequent diseases detection in healthcare industry. Among these attacks, hardware Trojans have created a major threatening issue in its security concern with multiple ways to crack the sensitive data or alter original functionality by doing malicious operations in biochips. In this paper, we present a systematic algorithm for the assignment of checkpoints required for error-recovery of available bioprotocols in case of hardware Trojans attacks in performing operations by biochip. Moreover, it can guide the placement and timing of checkpoints so that the result of an attack is reduced, and hence enhance the security concerns of digital microfluidic biochips. Comparative study with traditional checkpoint schemes demonstrate the superiority of the proposed algorithm without overhead of the bioprotocol completion time with higher error detection accuracy.

Crouch, A., Hunter, E., Levin, P. L..  2018.  Enabling Hardware Trojan Detection and Prevention through Emulation. 2018 IEEE International Symposium on Technologies for Homeland Security (HST). :1-5.

Hardware Trojans, implantable at a myriad of points within the supply chain, are difficult to detect and identify. By emulating systems on programmable hardware, the authors have created a tool from which to create and evaluate Trojan attack signatures and therefore enable better Trojan detection (for in-service systems) and prevention (for in-design systems).

Hossain, F. S., Shintani, M., Inoue, M., Orailoglu, A..  2018.  Variation-Aware Hardware Trojan Detection through Power Side-Channel. 2018 IEEE International Test Conference (ITC). :1-10.

A hardware Trojan (HT) denotes the malicious addition or modification of circuit elements. The purpose of this work is to improve the HT detection sensitivity in ICs using power side-channel analysis. This paper presents three detection techniques in power based side-channel analysis by increasing Trojan-to-circuit power consumption and reducing the variation effect in the detection threshold. Incorporating the three proposed methods has demonstrated that a realistic fine-grain circuit partitioning and an improved pattern set to increase HT activation chances can magnify Trojan detectability.

Cui, X., Wu, K., Karri, R..  2018.  Hardware Trojan Detection Using Path Delay Order Encoding with Process Variation Tolerance. 2018 IEEE 23rd European Test Symposium (ETS). :1-2.

The outsourcing for fabrication introduces security threats, namely hardware Trojans (HTs). Many design-for-trust (DFT) techniques have been proposed to address such threats. However, many HT detection techniques are not effective due to the dependence on golden chips, limitation of useful information available and process variations. In this paper, we data-mine on path delay information and propose a variation-tolerant path delay order encoding technique to detect HTs.

Xue, M., Bian, R., Wang, J., Liu, W..  2018.  A Co-Training Based Hardware Trojan Detection Technique by Exploiting Unlabeled ICs and Inaccurate Simulation Models. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1452-1457.

Integrated circuits (ICs) are becoming vulnerable to hardware Trojans. Most of existing works require golden chips to provide references for hardware Trojan detection. However, a golden chip is extremely difficult to obtain. In previous work, we have proposed a classification-based golden chips-free hardware Trojan detection technique. However, the algorithm in the previous work are trained by simulated ICs without considering that there may be a shift which occurs between the simulation and the silicon fabrication. It is necessary to learn from actual silicon fabrication in order to obtain an accurate and effective classification model. We propose a co-training based hardware Trojan detection technique exploiting unlabeled fabricated ICs and inaccurate simulation models, to provide reliable detection capability when facing fabricated ICs, while eliminating the need of fabricated golden chips. First, we train two classification algorithms using simulated ICs. During test-time, the two algorithms can identify different patterns in the unlabeled ICs, and thus be able to label some of these ICs for the further training of the another algorithm. Moreover, we use a statistical examination to choose ICs labeling for the another algorithm in order to help prevent a degradation in performance due to the increased noise in the labeled ICs. We also use a statistical technique for combining the hypotheses from the two classification algorithms to obtain the final decision. The theoretical basis of why the co-training method can work is also described. Experiment results on benchmark circuits show that the proposed technique can detect unknown Trojans with high accuracy (92% 97%) and recall (88% 95%).

Wang, C., Zhao, S., Wang, X., Luo, M., Yang, M..  2018.  A Neural Network Trojan Detection Method Based on Particle Swarm Optimization. 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT). :1-3.

Hardware Trojans (HTs) are malicious modifications of the original circuits intended to leak information or cause malfunction. Based on the Side Channel Analysis (SCA) technology, a set of hardware Trojan detection platform is designed for RTL circuits on the basis of HSPICE power consumption simulation. Principal Component Analysis (PCA) algorithm is used to reduce the dimension of power consumption data. An intelligent neural networks (NN) algorithm based on Particle Swarm Optimization (PSO) is introduced to achieve HTs recognition. Experimental results show that the detection accuracy of PSO NN method is much better than traditional BP NN method.

Bian, R., Xue, M., Wang, J..  2018.  Building Trusted Golden Models-Free Hardware Trojan Detection Framework Against Untrustworthy Testing Parties Using a Novel Clustering Ensemble Technique. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :1458-1463.

As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).

Cozzi, M., Galliere, J., Maurine, P..  2018.  Exploiting Phase Information in Thermal Scans for Stealthy Trojan Detection. 2018 21st Euromicro Conference on Digital System Design (DSD). :573-576.

Infrared thermography has been recognized for its ability to investigate integrated circuits in a non destructive way. Coupled to lock-in correlation it has proven efficient in detecting thermal hot spots. Most of the state of the Art measurement systems are based on amplitude analysis. In this paper we propose to investigate weak thermal hot spots using the phase of infrared signals. We demonstrate that phase analysis is a formidable alternative to amplitude to detect small heat signatures. Finally, we apply our measurement platform and its detection method to the identification of stealthy hardware Trojans.

Martin, H., Entrena, L., Dupuis, S., Natale, G. Di.  2018.  A Novel Use of Approximate Circuits to Thwart Hardware Trojan Insertion and Provide Obfuscation. 2018 IEEE 24th International Symposium on On-Line Testing And Robust System Design (IOLTS). :41-42.

Hardware Trojans have become in the last decade a major threat in the Integrated Circuit industry. Many techniques have been proposed in the literature aiming at detecting such malicious modifications in fabricated ICs. For the most critical circuits, prevention methods are also of interest. The goal of such methods is to prevent the insertion of a Hardware Trojan thanks to ad-hoc design rules. In this paper, we present a novel prevention technique based on approximation. An approximate logic circuit is a circuit that performs a possibly different but closely related logic function, so that it can be used for error detection or error masking where it overlaps with the original circuit. We will show how this technique can successfully detect the presence of Hardware Trojans, with a solution that has a smaller impact than triplication.

Inoue, T., Hasegawa, K., Kobayashi, Y., Yanagisawa, M., Togawa, N..  2018.  Designing Subspecies of Hardware Trojans and Their Detection Using Neural Network Approach. 2018 IEEE 8th International Conference on Consumer Electronics - Berlin (ICCE-Berlin). :1-4.

Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as "hardware Trojans'') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.

Ye, J., Yang, Y., Gong, Y., Hu, Y., Li, X..  2018.  Grey Zone in Pre-Silicon Hardware Trojan Detection. 2018 IEEE International Test Conference in Asia (ITC-Asia). :79-84.

Pre-Silicon hardware Trojan detection has been studied for years. The most popular benchmark circuits are from the Trust-Hub. Their common feature is that the probability of activating hardware Trojans is very low. This leads to a series of machine learning based hardware Trojan detection methods which try to find the nets with low signal probability of 0 or 1. On the other hand, it is considered that, if the probability of activating hardware Trojans is high, these hardware Trojans can be easily found through behaviour simulations or during functional test. This paper explores the "grey zone" between these two opposite scenarios: if the activation probability of a hardware Trojan is not low enough for machine learning to detect it and is not high enough for behaviour simulation or functional test to find it, it can escape from detection. Experiments show the existence of such hardware Trojans, and this paper suggests a new set of hardware Trojan benchmark circuits for future study.

Wang, Q., Geiger, R. L..  2017.  Visible but Transparent Hardware Trojans in Clock Generation Circuits. 2017 IEEE National Aerospace and Electronics Conference (NAECON). :354–357.

Hardware Trojans that can be easily embedded in synchronous clock generation circuits typical of what are used in large digital systems are discussed. These Trojans are both visible and transparent. Since they are visible, they will penetrate split-lot manufacturing security methods and their transparency will render existing detection methods ineffective.

Alsaiari, U., Gebali, F., Abd-El-Barr, M..  2017.  Programmable Assertion Checkers for Hardware Trojan Detection. 2017 1st Conference on PhD Research in Microelectronics and Electronics Latin America (PRIME-LA). :1–4.

Due to the increase in design complexity and cost of VLSI chips, a number of design houses outsource manufacturing and import designs in a way to reduce the cost. This results in a decrease of the authenticity and security of the manufactured product. Since product development involves outside sources, circuit designers can not guarantee that their hardware has not been altered. It is often possible that attackers include additional hardware in order to gain privileges over the original circuit or cause damage to the product. These added circuits are called ``Hardware Trojans''. In this paper, we investigate introducing necessary modules needed for detection of hardware Trojans. We also introduce necessary programmable logic fabric that can be used in the implementation of the hardware assertion checkers. Our target is to utilize the provided programable fabric in a System on Chip (SoC) and optimize the hardware assertion to cover the detection of most hardware trojans in each core of the target SoC.

K, S. K., Sahoo, S., Mahapatra, A., Swain, A. K., Mahapatra, K. K..  2017.  Analysis of Side-Channel Attack AES Hardware Trojan Benchmarks against Countermeasures. 2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). :574–579.

Hardware Trojan (HT) is one of the well known hardware security issue in research community in last one decade. HT research is mainly focused on HT detection, HT defense and designing novel HT's. HT's are inserted by an adversary for leaking secret data, denial of service attacks etc. Trojan benchmark circuits for processors, cryptography and communication protocols from Trust-hub are widely used in HT research. And power analysis based side channel attacks and designing countermeasures against side channel attacks is a well established research area. Trust-Hub provides a power based side-channel attack promoting Advanced Encryption Standard (AES) HT benchmarks for research. In this work, we analyze the strength of AES HT benchmarks in the presence well known side-channel attack countermeasures. Masking, Random delay insertion and tweaking the operating frequency of clock used in sensitive operations are applied on AES benchmarks. Simulation and power profiling studies confirm that side-channel promoting HT benchmarks are resilient against these selected countermeasures and even in the presence of these countermeasures; an adversary can get the sensitive data by triggering the HT.

Esirci, F. N., Bayrakci, A. A..  2017.  Hardware Trojan Detection Based on Correlated Path Delays in Defiance of Variations with Spatial Correlations. Design, Automation Test in Europe Conference Exhibition (DATE), 2017. :163–168.

Hardware Trojan (HT) detection methods based on the side channel analysis deeply suffer from the process variations. In order to suppress the effect of the variations, we devise a method that smartly selects two highly correlated paths for each interconnect (edge) that is suspected to have an HT on it. First path is the shortest one passing through the suspected edge and the second one is a path that is highly correlated with the first one. Delay ratio of these paths avails the detection of the HT inserted circuits. Test results reveal that the method enables the detection of even the minimally invasive Trojans in spite of both inter and intra die variations with the spatial correlations.

Hasegawa, K., Yanagisawa, M., Togawa, N..  2017.  Trojan-Feature Extraction at Gate-Level Netlists and Its Application to Hardware-Trojan Detection Using Random Forest Classifier. 2017 IEEE International Symposium on Circuits and Systems (ISCAS). :1–4.

Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.

Cornell, N., Nepal, K..  2017.  Combinational Hardware Trojan Detection Using Logic Implications. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS). :571–574.

This paper provides a proof-of-concept demonstration of the potential benefit of using logical implications for detection of combinational hardware trojans. Using logic simulation, valid logic implications are selected and added to to the checker circuitry to detect payload delivery by a combinational hardware trojan. Using combinational circuits from the ISCAS benchmark suite, and a modest hardware budget for the checker, simulation results show that the probability of a trojan escaping detection using our approach was only 16%.