High-level synthesis of key based obfuscated RTL datapaths

Islam, Sheikh Ariful; Katkoori, Srinivas

doi:10.1109/isqed.2018.8357321

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…A strong PUF can support a large number of complex CRPs with physical access to the PUF for a query such that an attacker cannot generate correct response given finite resources and time [14,17,31]. While a weak PUF has only a few CRPs, which makes it difficult for the attack and prediction techniques, hence in this paper, we consider strong PUF.…”

Section: Strong Puf Architecturesmentioning

confidence: 99%

Machine Learning Attacks and Countermeasures for PUF-Based IoT Edge Node Security

2020

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The Internet of things (IoT) ecosystem has grown exponentially with the convergence of various technologies such as deep learning, sensor systems, and advances in computing platforms. With such a highly pervasive nature of "smart" devices, the nature of data being collected and processed can be increasingly private and require safeguards to ensure the data's integrity and security. Physically unclonable functions (PUFs) have emerged as a lightweight, viable security protocol in the Internet of Things (IoT) framework. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. In this work, we show that knowledge of the underlying PUF structure is unnecessary to clone a PUF. We tackle the problem of cloning PUF-based edge nodes in different settings such as unencrypted, encrypted, and obfuscated challenges in an IoT framework. We present a novel non-invasive, architecture-independent, machine learning attack for robust PUF designs and can handle encryption and obfuscation-based security measures on the transmitted challenge response pairs (CRPs). We show that the proposed framework can successfully clone different PUF architectures, including those encrypted using two (2) different encryption protocols in DES and AES and with varying degrees of obfuscation. We also show that the proposed approach outperforms a two-stage brute force attack model. Finally, we offer a machine learning-based countermeasure, a discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server. Keywords Machine learning • Internet of things • Physically unclonable functions • Edge node security This article is part of the topical collection "Hardware-Assisted Security Solutions for Electronic Systems" guest edited by

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Section: Strong Puf Architecturesmentioning

confidence: 99%

Machine Learning Attacks and Countermeasures for PUF-Based IoT Edge Node Security

2020

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“…Strong PUFs: A strong PUF can support a large number of complex CRPs with physical access to the PUF for a query such that an attacker cannot generate correct response given finite resources and time [17]- [19]. While a weak PUF has only few CRPs which makes it difficult for the attack and prediction techniques, hence in this paper we consider strong PUF.…”

Section: Background and Related Workmentioning

confidence: 99%

Machine Learning Based IoT Edge Node Security Attack and Countermeasures

Laguduva

Islam

Aakur

et al. 2019

2019 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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Advances in technology have enabled tremendous progress in the development of a highly connected ecosystem of ubiquitous computing devices collectively called the Internet of Things (IoT). Ensuring the security of IoT devices is a high priority due to the sensitive nature of the collected data. Physically Unclonable Functions (PUFs) have emerged as critical hardware primitive for ensuring the security of IoT nodes. Malicious modeling of PUF architectures has proven to be difficult due to the inherently stochastic nature of PUF architectures. Extant approaches to malicious PUF modeling assume that a priori knowledge and physical access to the PUF architecture is available for malicious attack on the IoT node. However, many IoT networks make the underlying assumption that the PUF architecture is sufficiently tamper-proof, both physically and mathematically. In this work, we show that knowledge of the underlying PUF structure is not necessary to clone a PUF. We present a novel non-invasive, architecture independent, machine learning attack for strong PUF designs with a cloning accuracy of 93.5% and improvements of up to 48.31% over an alternative, two-stage brute force attack model. We also propose a machine-learning based countermeasure, discriminator, which can distinguish cloned PUF devices and authentic PUFs with an average accuracy of 96.01%. The proposed discriminator can be used for rapidly authenticating millions of IoT nodes remotely from the cloud server.

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“…In [7] and [8], high-level transformations are used to protect digital signal processing (DSP) circuits. In [9] and [10], keybased obfuscation is added during HLS by extending the HLS process. In these works, the goal is to obtain obfuscated RTL and to provide protection during manufacturing or for thirdparty vendors of IPs, and HLS is considered to be a trusted design step.…”

Section: A Ip Protectionmentioning

confidence: 99%