Architectural Diversity: Bio-Inspired Hardware Security for FPGAs

Mahmud, Shakil; Olney, Brooks; Karam, Robert

doi:10.1109/ivsw.2018.8494854

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…MUTARCH is a mutable FPGA architecture that offers security through diversity. It was originally proposed by R. Karam et al in 2017 [133] and further extended in 2018 [134]. In this architecture, bitstreams are obfuscated through a combination of a physical and a logical key.…”

Section: ) Mutarchmentioning

confidence: 99%

FPGA Bitstream Modification: Attacks and Countermeasures

Moraitis

2023

IEEE Access

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Advances in FPGA technology in recent years have resulted in an expansion of its usage in a very wide spectrum of applications. Apart from serving the traditional prototyping purposes, FPGAs are currently regarded as an integral part of embedded systems used in many industries, including communication, medical, aerospace, automotive, and military. Moreover, the emerging trend of AI has found FPGAs to be at the technological forefront with their use as deep learning acceleration platforms. The demand for FPGAs has grown to the point that major companies (e.g. Amazon) are offering cloudbased access to FPGAs, known as FPGA-as-a-Service. In many applications, FPGAs handle sensitive data and/or host cryptographic algorithm implementations. These FPGAs are not always located in a tamperresistant environment, which makes their security a major concern, especially in light of the ever-growing number of publications demonstrating effective attacks specifically tailored to exploit the physical traits of FPGA implementations. In this survey, we cover the subset of those attacks that involve tampering with the FPGA configuration bitstream. We start by discussing how the FPGA vendors attempt to protect their products and how malicious parties try to overcome this protection. We then proceed to present the different bitstream modification attacks that can be found in the literature organized according to their targets. Finally, we present various countermeasures that can be deployed, drawing on bibliographic references from works specifically focused on FPGA bitstream protection, as well as those initially proposed for different purposes or devices that can be adapted for bitstream protection.

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Section: ) Mutarchmentioning

confidence: 99%

FPGA Bitstream Modification: Attacks and Countermeasures

Moraitis

2023

IEEE Access

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“…This has inspired researchers to develop newer and more sophisticated security protocols, including protocols that are inspired by biology and species diversity. There have been many research endeavors that propose bio-inspired security protocols for the secure deployment of hardware [26,37,59,40] and software [11,4,28], as well as security protocols to protect networks and cyberspace [44,15] and to ensure data privacy and security [22,12]. In general, diversity of a species at the genetic level is necessary for the overall survival of that species.…”

Section: Bio-diversity Inspired Securitymentioning

confidence: 99%

Diverse, Neural Trojan Resilient Ecosystem of Neural Network IP

Olney

Karam

2022

J. Emerg. Technol. Comput. Syst.

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Adversarial machine learning is a prominent research area aimed towards exposing and mitigating security vulnerabilities in AI/ML algorithms and their implementations. Data poisoning and neural Trojans enable an attacker to drastically change the behavior and performance of a Convolutional Neural Network (CNN) merely by altering some of the input data during training. Such attacks can be catastrophic in the field, e.g. for self-driving vehicles. In this paper, we propose deploying a CNN as an ecosystem of variants , rather than a singular model. The ecosystem is derived from the original trained model, and though every derived model is structurally different, they are all functionally equivalent to the original and each other. We propose two complementary techniques: stochastic parameter mutation , where the weights θ of the original are shifted by a small, random amount, and a delta-update procedure which functions by XOR’ing all of the parameters with an update file containing the Δθ values. This technique is effective against transferability of a neural Trojan to the greater ecosystem by amplifying the Trojan’s malicious impact to easily detectable levels; thus, deploying a model as an ecosystem can render the ecosystem more resilient against a neural Trojan attack.

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