A practical application of game theory to optimize selection of hardware Trojan detection strategies

Graf, Jonathan; Batchelor, W.E.; Harper, Scott; Marlow, Ryan; Carlisle, Edward; Athanas, Peter

doi:10.1007/s41635-019-00089-3

Cited by 13 publications

(3 citation statements)

References 40 publications

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“…A novel game model is framed in [22] to analyze the relation between the manufacturer (attacker) and the IC testing unit (defender). In this paper [23], a strategic game is modelled with two players to determine the suitable strategy for both defender and intruder. A software framework is also presented, which provides the mathematical exploration and also provides the solution of the game.…”

Section: Related Workmentioning

confidence: 99%

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

Murugesan¹,

Manickam²

2020

IJIES

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In modern electronic design, hardware Trojan has emerged as a major threat in the hardware security. To detect the hardware Trojan is a major problem in testing process because of their inherent concealed nature. In this work, we propose a deep learning-based Trojan classification approach, which extracts the optimal feature to indicate the nets affected by the Trojan module. In this approach, a handcrafted algorithm along with the structural report is also analyzed for extracting further features of the gate level netlist, which stamp out the requirement of golden chip. This detection technique is also validated using game theoretical approach, which is modelled as zero-sum game between the attacker and the defender. The Simulation is employed on ISCAS’85, ISCAS’89 and Trust-HUB circuits and the deep learning algorithm performs the best in detection and classification of Trojan type with an average True positive rate of 96.69% and an accuracy of 96.25%.

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Section: Related Workmentioning

confidence: 99%

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

Murugesan¹,

Manickam²

2020

IJIES

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“…Combined with simple logic inference, the total can be realized through analysis. Recently, lots of studies have applied the research of Game Theory to the application of deep learning [13,14] , economics [15][16][17][18][19] , and information security [20] .…”

Section: Introductionmentioning

confidence: 99%

The formation analysis of Nash equilibriums in bi-matrix game generated by propositional formulas

Zhao

Lee

Li³

et al. 2023

International Conference on Pure, Applied, and Computational Mathematics (PACM 2023)

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In Game Theory, the computation and counting of equilibrium and the structural features of equilibrium are major research topics. The existence of equilibrium naturally forms a decision problem. The Nash equilibrium composed of mixed strategies always exists in the matrix and double matrix games. Therefore, calculating Nash equilibrium is the main task in the matrix and double matrix games. For the double matrix game, the measure of its Nash equilibrium can be based on the stability implied in the Nash equilibrium definition. Besides, a propositional formula can construct a two-player game given as a bi-matrix by specifying a proper finite set of strategies and adding a transformation. In this study, we investigate the development of Nash equilibriums by analyzing the structure-property of bi-matrix. We examine the characterization of Nash equilibrium creation and the relationship between satisfiable assignments of formula and Nash equilibriums of generated Game based on the characterization of bi-matrix formation and the stability principle of Nash equilibriums. The method from analysis and logic helps examine some relationships between combination optimization issues in graph theory and game systems by examining the structure of bi-matrix and combining the stability principle of Nash equilibriums to research the characterization of Nash equilibriums.

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“…Hardware security, however, suffers from a number of market failures, including information asymmetry, prisoner's dilemmas, misaligned incentives, and free riding 29 . Research has been conducted using game theory to detect and protect against hardware Trojans 30 . However, in general, the research on the economics of security investment for hardware security and trust is an undeveloped area.…”

Section: Introductionmentioning

confidence: 99%

On metrics and prioritization of investments in hardware security

Collier

Briglia

Finkelston

et al. 2023

Systems Engineering

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The security risks posed by electronics are numerous. There are typically a variety of risk‐reducing countermeasures for a given system or across an enterprise. Each countermeasure is associated with both a level of risk reduction and its lifecycle costs. Given budgetary constraints, risk managers and systems engineers must determine what combinations of countermeasures cost‐effectively maximize risk reduction, and what metrics best guide the investment process. In this paper, we seek to answer these questions through exploration of risk reduction metrics from the field of security economics, including the benefit/cost ratio, return on security investment (ROSI), expected benefit of information security (EBIS), and expected net benefit of information security (ENBIS). The results suggest that ratio‐based metrics are not strongly correlated with risk reduction, while EBIS is equivalent to risk reduction and ENBIS is equal to risk reduction minus cost.

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A practical application of game theory to optimize selection of hardware Trojan detection strategies

Cited by 13 publications

References 40 publications

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

An Optimal Feature Extraction using Deep Learning Technique for Trojan Detection and Validation using Game Theory

The formation analysis of Nash equilibriums in bi-matrix game generated by propositional formulas

On metrics and prioritization of investments in hardware security

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