Advanced Techniques for Designing Stealthy Hardware Trojans

Tsoutsos, Nektarios Georgios; Konstantinou, Charalambos; Maniatakos, Michail

doi:10.1145/2593069.2596668

Cited by 23 publications

(17 citation statements)

References 7 publications

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“…The replicable partitions are symmetrically-positioned ones, as similar current loads are favorable on these regions. For example, regions (1, 2), (2, 1), (2,3) and (3,2) in Fig. 5 are replicable with each other but not with any other region.…”

Section: Other Normalization Factorsmentioning

confidence: 90%

“…2 to handle the process variation, but these methods are not powerful enough for detecting ultra-small HTHs. 2,3 Various e®orts are performed to overcome the challenges of process variation. 5,9 Partial activation of VLSI chips is used in Ref.…”

Section: Trojan Detection Techniquesmentioning

confidence: 99%

“…For example, in the case of placing c499 in region (2,2), current-load shares in regions (1, 2), (2, 1), (2,3) and (3,2) are supposed to be equal. However, the results show that the real current loads vary from 8.2% to 11.6% of the total current, in these regions.…”

Section: Pad Coordinatesmentioning

confidence: 99%

“…1 Moreover, HTHs may produce analog outputs without changing the digital behavior of the circuit. 3 For these reasons, instead of classic testing, side-e®ect analysis techniques are used to perceive anomalies which may arise in the characteristics of a VLSI circuit, owing to the presence of an HTH. One of the most common forms of such techniques is power-signal analysis,¯rstly adopted in Ref.…”

Section: Introductionmentioning

confidence: 99%

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A Trust-Driven Placement Approach: A New Perspective on Design for Hardware Trust

Shekarian

Zamani

2015

J CIRCUIT SYST COMP

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During the last few years, hardware Trojan horses (HTHs) have become one of the most important threats to the security of very large scale integrated (VLSI) chips. Many e®orts have been made to facilitate the process of HTH detection, mostly based on the power analysis of chips. The techniques would be more bene¯cial if trust-driven techniques are used during the system design. Whereas design for hardware trust (DFHT) is one of the¯elds of interest, most current approaches include ad-hoc and gate-level design techniques. This paper discusses the advantage of physical-level design approaches with integrated strategies for improving the HTH-detection probability. As a proof of concept, a placement technique is presented with the goal of enhancing the ability of HTH detection techniques based on local power signal analysis. Our results show that the background e®ects on power pads can be leveraged by a simple partitioning-based placement algorithm. Minimizing the background e®ects leads to a better Trojan-to-background-e®ect ratio and more (by about 1.7 times) Trojan detectability.

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Section: Other Normalization Factorsmentioning

confidence: 90%

Section: Trojan Detection Techniquesmentioning

confidence: 99%

Section: Pad Coordinatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Trust-Driven Placement Approach: A New Perspective on Design for Hardware Trust

Shekarian

Zamani

2015

J CIRCUIT SYST COMP

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“…This directive takes three arguments: the first is a constant expression corresponding to the bit expansion of φ(kφ) −1 N , while the remaining arguments are the names of two macros implementing squaring (for bits 0 in the bit expansion), as well as squaring and multiplying (for bits 1). 5 Using the provided bit expansion, the compiler generates a list of macro calls to either the squaring macro, or the squaring and multiplying one, mapping each such call to the corresponding bit in the bit expansion (starting from the least significant one, without generating any branching code). This list of macros is used at runtime to compute the decryption of the first ciphertext argument of function G. The compiler completes the implementation of function G, by referencing library code to perform the following steps: (i) generate new encryptions 0 (using previous random encryptions of zero as randomness seed); (ii) depending on the result of a Leq test (eq.…”

Section: Library Multiplication and Function Gmentioning

confidence: 99%

Cryptoleq: A Heterogeneous Abstract Machine for Encrypted and Unencrypted Computation

Mazonka

Tsoutsos

Maniatakos

2016

IEEE Trans.Inform.Forensic Secur.

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The rapid expansion and increased popularity of cloud computing comes with no shortage of privacy concerns about outsourcing computation to semi-trusted parties. Leveraging the power of encryption, in this paper we introduce Cryptoleq: an abstract machine based on the concept of One Instruction Set Computer, capable of performing general-purpose computation on encrypted programs. The program operands are protected using the Paillier partially homomorphic cryptosystem, which supports addition on the encrypted domain. Full homomorphism over addition and multiplication, which is necessary for enabling general-purpose computation, is achieved by inventing a heuristically obfuscated software re-encryption module written using Cryptoleq instructions and blended into the executing program. Cryptoleq is heterogeneous, allowing mixing encrypted and unencrypted instruction operands in the same program memory space. Programming with Cryptoleq is facilitated using an enhanced assembly language that allows development of any advanced algorithm on encrypted datasets. In our evaluation, we compare Cryptoleq's performance against a popular fully homomorphic encryption library, and demonstrate correctness using a typical Private Information Retrieval problem.

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Lightweight Fault Tolerance for Secure Aggregation of Homomorphic Data

Tsoutsos

Maniatakos

2018

Internet of Things

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Advanced Techniques for Designing Stealthy Hardware Trojans

Cited by 23 publications

References 7 publications

A Trust-Driven Placement Approach: A New Perspective on Design for Hardware Trust

A Trust-Driven Placement Approach: A New Perspective on Design for Hardware Trust

Cryptoleq: A Heterogeneous Abstract Machine for Encrypted and Unencrypted Computation

Lightweight Fault Tolerance for Secure Aggregation of Homomorphic Data

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