On Reverse Engineering-Based Hardware Trojan Detection

Bao, Chongxi; Forte, Domenic; Srivastava, Ankur

doi:10.1109/tcad.2015.2488495

Cited by 135 publications

(45 citation statements)

References 20 publications

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“…Therefore, the geochemical and competitive removal behavior of coexisting As and Sb under heterogeneous aqueous environment is worth understanding. Many researchers [30][31][32] have investigated the simultaneous removal performance of As and Sb in the multicomponent environment. The factors, such as pH, coagulant type and dosage, initial contaminant levels and NOM, affect the As or Sb adsorption onto the surface of Fe minerals [33,34].…”

Section: Introductionmentioning

confidence: 99%

Influence of pH and Contaminant Redox Form on the Competitive Removal of Arsenic and Antimony from Aqueous Media by Coagulation

et al. 2018

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In most countries, arsenic (As) and antimony (Sb) are regulated pollutants, due to their significant impacts on the environment and human health. Iron-based (Fe) coagulants play a fundamental role in the removal of both elements from aqueous media. This study aims to investigate the competitive removal of As and Sb in relation to Fe solubility. Coagulation experiments were conducted in synthetic water under various pH and contaminant loading, using ferric chloride (FC) as a coagulant. In the single system, the pentavalent species significantly reduced the Fe solubility and thereby enhanced the mobility of As and Sb under these environmental conditions. The coexistence of pentavalent and trivalent species in the binary system considerably decreases the Fe solubility at acidic conditions while enhancing the dissolution under alkaline conditions, thus affecting the overall removal of both species. The presence of four redox species in the quaternary system decreases the Fe solubility remarkably over a wide pH range, with better Sb removal, as compared to As under similar conditions. The adsorption study of the single system showed a decrease in As(V) adsorption capacity at higher concentration, while in the binary system, the Sb(III) showed strong adsorption potential, compared to other species. In the quaternary system, the presence of all four redox species has a synergistic effect on total Sb adsorption, in comparison to the total As. Furthermore, the results of Fourier transform infrared (FT-IR) analysis of FC composite contaminant flocs confirm that the combined effect of charge neutralization and inner sphere complexation might be a possible removal mechanism. These findings may facilitate the fate, transport and comparative removal of redox species in the heterogeneous aquatic environment.

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Section: Introductionmentioning

confidence: 99%

Influence of pH and Contaminant Redox Form on the Competitive Removal of Arsenic and Antimony from Aqueous Media by Coagulation

et al. 2018

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“…Currently, most of the researches on hardware-Trojan detection methods are reverse engineering, 10,11 logic testing, [12][13][14] and side-channel signal analysis, [15][16][17][18] which called post-silicon detection methods. However, due to the difficulty of the acquisition of golden chips, influenced by noise easily, high cost, the requirement of special equipment, and other reasons, the post-silicon detection methods are not effective in practical applications.…”

Section: Introductionmentioning

confidence: 99%

A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

Dong

Chen

Guo

et al. 2019

International Journal of Distributed Sensor Networks

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With the development of the Internet of Things, smart devices are widely used. Hardware security is one key issue in the security of the Internet of Things. As the core component of the hardware, the integrated circuit must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise, and are suitable for the safe detection of a very large-scale integration. Therefore, more and more researchers are paying attention to the pre-silicon detection method. In this study, we propose a machine-learning-based hardware-Trojan detection method at the gate level. First, we put forward new Trojan-net features. After that, we use the scoring mechanism of the eXtreme Gradient Boosting to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained and detected based on the new feature set by the eXtreme Gradient Boosting algorithm, respectively. The experimental results show that the proposed method can obtain 89.84% average Recall, 86.75% average F-measure, and 99.83% average Accuracy, which is the best detection result among existing machine-learning-based hardware-Trojan detection methods.

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“…To defeat HTs and prevent HT-infected chips from being supplied to the market, researchers have proposed various HT detection techniques over the past decade [1], [2]. Benefiting from the development of training algorithms and computational power, very recent research shows a new trend in adopting machine learning (ML) approaches for HT detection [3], [4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Automatic Hardware Trojan Generation Platform

Liu²,

O’Neill

2019

2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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Over the past 10 years, various Hardware Trojan (HT) detection techniques have been proposed by the research community. However, the development of HT benchmark suites for testing and evaluating HT detection techniques lags behind. The number of HT-infected circuits available in current public HT benchmarks is somewhat limited and the circuits lack diversity in structure. Therefore, this paper proposes a new method to generate HTs using a highly configurable generation platform based on transition probability. The generation platform is highly configurable in terms of the HT trigger condition, trigger type, payload type and in the number and variety of HT-infected circuits that can be generated. In this research the transition probability of netlists is employed to identify rarely activated internal nodes to target for HT insertion rather than functional simulation as utilised in previous research. The authors believe transition probability provides a more realistic reflection of the netlist activity for use in determining the appropriate position for HT insertion. Finally, the generated HT-infected circuits are tested by a machine learning (ML)-based HT detection technique, which is known as Controllability and Observability for HT Detection (COTD). The resulting false positive and false negative rates illustrate the feasibility of the benchmark suite.

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On Reverse Engineering-Based Hardware Trojan Detection

Cited by 135 publications

References 20 publications

Influence of pH and Contaminant Redox Form on the Competitive Removal of Arsenic and Antimony from Aqueous Media by Coagulation

Influence of pH and Contaminant Redox Form on the Competitive Removal of Arsenic and Antimony from Aqueous Media by Coagulation

A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things

An Improved Automatic Hardware Trojan Generation Platform

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