Hardware Trojan Detection using Xilinx Vivado

Marlow, Ryan; Harper, Scott; Batchelor, W.E.; Graf, Jonathan

doi:10.1109/naecon.2018.8556648

Cited by 1 publication

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The strategy σ D2 (SCOA) is a static controllability/ observability and analysis method based on the theory presented in [45] using the implementation we published 19 in [46]. The method makes use of static controllability and observability analysis [47] information as the basis of an unsupervised clustering analysis, which allows the separation of signals that are components of hardware Trojans and those that are not.…”

Section: Defender Strategiesmentioning

confidence: 99%

“…The method makes use of static controllability and observability analysis [47] information as the basis of an unsupervised clustering analysis, which allows the separation of signals that are components of hardware Trojans and those that are not. The implementation makes use of the TCL interfaces exposed by Xilinx Vivado and performs the k-means clustering using the scikit-learn Python library [46,48]. We assume this automated tool would be simple to use (16 h of labor and machine time) and modestly priced if sold on the market ($15,000 license).…”

Section: Defender Strategiesmentioning

confidence: 99%

“…That is, instead of simulating 1 million random input vectors and stopping, random inputs would be generated and simulated until some pre-set high percentage of the wires in the circuit have transitioned from 1-to-0 or 0-to-1 19. The implementation details of our work in[46] are interesting but they diverge from this discussion here. The original contribution of the paper was the implementation, not the algorithm.…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

A wide variety of Hardware Trojan countermeasures have been developed, but less work has been done to determine which are optimal for any given design. To address this, we consider not only metrics related to the performance of the countermeasure, but also the likely action of an adversary given their goals. Trojans are inserted by an adversary to accomplish an end, so these goals must be considered and quantified in order to predict these actions. The model presented here builds upon a security economic approach that models the adversary and defender motives and goals in the context of empirically derived countermeasure efficacy metrics. The approach supports formation of a two-player strategic game to determine optimal strategy selection for both adversary and defender. A game may be played in a variety of contexts, including consideration of the entire design lifecycle or only a step in product development. As a demonstration of the practicality of this approach, we present an experiment that derives efficacy metrics from a set of countermeasures (defender strategies) when tested against a taxonomy of Trojans (adversary strategies). We further present a software framework, GameRunner, that automates not only the solution to the game but also mathematical and graphical exploration of "what if" scenarios in the context of the game. GameRunner can also issue "prescriptions," a set of commands that allows the defender to automate the application of the optimal defender strategy to their circuit of concern. Finally, we include a discussion of ongoing work to include additional software tools, a more advanced experimental framework, and the application of irrationality models to account for players who make subrational decisions.

show abstract