A Survey of Hardware Trojan Taxonomy and Detection

By M. Tehranipoor, Farinaz Koushanfar. 2010

1,357 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering, Hardware and Architecture

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Citations in the corpus

PoTrojan: powerful neural-level trojan designs in deep learning models (2018, background): “In fact, hardware security has been extensively studied that hardware trojans comprised of a small amount of transistors could be inserted in very-large-scale integration (VLSI) circuits without affecting the normal function of the host circuits [5].”
Neural Trojans (2017, background): “The neural Trojans are analogous to hardware Trojans [27].”
Unsupervised Learning Trojan (2014, methodology): “A proposed taxonomy to describe hardware Trojans is given in Tehranipoor & Koushanfar (2010).”
A Comprehensive Study of Security of Internet-of-Things (2017, methodology): “have been proposed in the last decade [24], [91], [92].”
TrojanNet: Embedding Hidden Trojan Horse Models in Neural Networks (2020, methodology): “In this paper we do not elaborate on mechanisms to hide the Trojan trigger, which has been covered extensively in prior work (Tehranipoor & Koushanfar, 2010; Felt et al., 2011), and focus on the nov-elty of concealing a Trojan network inside another model.”
Benchmarking of Hardware Trojans and Maliciously Affected Circuits (2017, background): “Over the past few years, there have been efforts to develop comprehensive hardware Trojan taxonomies based on Trojan implementation and effect [7].”
Counterfeit Integrated Circuits: A Rising Threat in the Global Semiconductor Supply Chain (2014, background): “Such components can either act as a silicon time bomb where the device can behave differently under certain conditions or act as a backdoor where secret information from the chip can be sent out to an adversary [32].”
Hardware Trojan Attacks: Threat Analysis and Countermeasures (2014, background): “KEYWORDS | Hardware intellectual property (IP) trust; hardware obfuscation; hardware Trojan attacks; self-referencing; side-channel analysis; Trojan detection; Trojan taxonomy; Trojan tolerance”
Quantum random number generators (2016, background): “This is an example of the more general threat of hardware trojans, which are different kinds of malicious modifications that are inserted at the hardware level (Tehranipoor and Koushanfar, 2010).”
A Primer on Hardware Security: Models, Methods, and Metrics (2014, background): “Microcontrollers are extensively used in embedded systems, and they are equipped with ‘‘fuse bits’’ to prevent unauthorized users from reading or modifying selected sections of its memory.”
On design vulnerability analysis and trust benchmarks development (2013, positive): “Using these methodologies, we have developed a large number of trust benchmarks that are available for use by the public, as well as researchers and practitioners in the field.”
Verifiable ASICs (2016, background): “This assumes that V has access to auxiliary inputs that have been precomputed offline; auxiliary inputs are independent of the input to C .”
Hardware Trojan Attacks on Neural Networks (2018, methodology): “In the literature, various types of hardware Trojans have been developed [15], [16], [26], [27].”