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2020-02-26
Juretus, Kyle, Savidis, Ioannis.  2019.  Increasing the SAT Attack Resiliency of In-Cone Logic Locking. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). :1–5.

A method to increase the resiliency of in-cone logic locking against the SAT attack is described in this paper. Current logic locking techniques provide protection through the addition of circuitry outside of the original logic cone. While the additional circuitry provides provable security against the SAT attack, other attacks, such as the removal attack, limit the efficacy of such techniques. Traditional in-cone logic locking is not prone to removal attacks, but is less secure against the SAT attack. The focus of this paper is, therefore, the analysis of in-cone logic locking to increase the security against the SAT attack, which provides a comparison between in-cone techniques and newly developed methodologies. A novel algorithm is developed that utilizes maximum fanout free cones (MFFC). The application of the algorithm limits the fanout of incorrect key information. The MFFC based algorithm resulted in an average increase of 61.8% in the minimum number of iterations required to complete the SAT attack across 1,000 different variable orderings of the circuit netlist while restricted to a 5% overhead in area.

2019-03-15
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.

2015-04-30
Shila, D.M., Venugopal, V..  2014.  Design, implementation and security analysis of Hardware Trojan Threats in FPGA. Communications (ICC), 2014 IEEE International Conference on. :719-724.

Hardware Trojan Threats (HTTs) are stealthy components embedded inside integrated circuits (ICs) with an intention to attack and cripple the IC similar to viruses infecting the human body. Previous efforts have focused essentially on systems being compromised using HTTs and the effectiveness of physical parameters including power consumption, timing variation and utilization for detecting HTTs. We propose a novel metric for hardware Trojan detection coined as HTT detectability metric (HDM) that uses a weighted combination of normalized physical parameters. HTTs are identified by comparing the HDM with an optimal detection threshold; if the monitored HDM exceeds the estimated optimal detection threshold, the IC will be tagged as malicious. As opposed to existing efforts, this work investigates a system model from a designer perspective in increasing the security of the device and an adversary model from an attacker perspective exposing and exploiting the vulnerabilities in the device. Using existing Trojan implementations and Trojan taxonomy as a baseline, seven HTTs were designed and implemented on a FPGA testbed; these Trojans perform a variety of threats ranging from sensitive information leak, denial of service to beat the Root of Trust (RoT). Security analysis on the implemented Trojans showed that existing detection techniques based on physical characteristics such as power consumption, timing variation or utilization alone does not necessarily capture the existence of HTTs and only a maximum of 57% of designed HTTs were detected. On the other hand, 86% of the implemented Trojans were detected with HDM. We further carry out analytical studies to determine the optimal detection threshold that minimizes the summation of false alarm and missed detection probabilities.