Deep-Learning-Based Security Evaluation on Authentication Systems Using Arbiter PUF and Its Variants

Yashiro, Risa; Machida, Takanori; Iwamoto, Mitsugu; Sakiyama, Kazuo

doi:10.1007/978-3-319-44524-3_16

Cited by 23 publications

(15 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DAPUF is also evaluated under the ML attack. In [10], the authors reported that an attack on DAPUF using Deep Learning (DL) was unsuccessful. However, they also reported that DAPUF has a lot of unstable responses, which makes it unsuitable for conventional authentication system.…”

Section: Introductionmentioning

confidence: 99%

<i>Q</i>-Class Authentication System for Double Arbiter PUF

Yashiro

Sugawara

Iwamoto

et al. 2018

IEICE Trans. Fundamentals

Self Cite

View full text Add to dashboard Cite

Physically Unclonable Function (PUF) is a cryptographic primitive that is based on physical property of each entity or Integrated Circuit (IC) chip. It is expected that PUF be used in security applications such as ID generation and authentication. Some responses from PUF are unreliable, and they are usually discarded. In this paper, we propose a new PUF-based authentication system that exploits information of unreliable responses. In the proposed method, each response is categorized into multiple classes by its unreliability evaluated by feeding the same challenges several times. This authentication system is named Q-class authentication, where Q is the number of classes. We perform experiments assuming a challenge-response authentication system with a certain threshold of errors. Considering 4-class separation for 4-1 Double Arbiter PUF, it is figured out that the advantage of a legitimate prover against a clone is improved form 24% to 36% in terms of success rate. In other words, it is possible to improve the tolerance of machine-learning attack by using unreliable information that was previously regarded disadvantageous to authentication systems.

show abstract

Section: Introductionmentioning

confidence: 99%

<i>Q</i>-Class Authentication System for Double Arbiter PUF

Yashiro

Sugawara

Iwamoto

et al. 2018

IEICE Trans. Fundamentals

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ensemble learning is a technique of merging the predictions from several classifiers to generate a robust classifier. An emerging machine learning algorithm namely deep learning (DL) was also used to perform modeling attack on strong PUFs as proposed by Yashiro et al [8]. They described that DL has superior performance compared to conventional machine-learning methods on a benchmark test in the field of image recognition.…”

Section: ) Attacks On Strong Pufsmentioning

confidence: 99%

“…The first category is designbased where the defense mechanism is added in the architectural design of the PUFs such as adding non-linearity using XOR logics [6], [7], modifying transistor-level design [8], exploiting FPGA blocks [9], [10] and analogizing the digital components [11]. The second category is the obfuscation-based where the defense mechanism is performed masking of either challenges [12] or responses [2], [13].…”

Section: Background Of the Problemmentioning

confidence: 99%

“…www.ijacsa.thesai.org Table 1, the defense mechanisms that were proposed can be divided into three categories namely design, obfuscation and access control. The design category consists of adding non-linearity using XOR logics [6], [7], configuring transistor's gate sizes [62], augmenting interconnects [63], tightening the layout condition [8], exploiting FPGA resources and reconfigurability [9], [10] and modify current PUF design blocks by using analog blocks [11] and reducing feed-forward stages [64]. These defense mechanisms provide resistance to machine learning modeling attacks however they come with certain shortcomings.…”

Section: Comparison Analysis Of Defense Mechanisms Against Attacksmentioning

confidence: 99%

“…They described that DL has superior performance compared to conventional machine-learning methods on a benchmark test in the field of image recognition. DL is defined by Yashiro et al [8] as a multilayer neural network where it has more than two layers. The output of a layer acts as input for the following layer.…”

Section: ) Attacks On Strong Pufsmentioning

confidence: 99%

See 2 more Smart Citations

Defense Mechanisms against Machine Learning Modeling Attacks on Strong Physical Unclonable Functions for IOT Authentication: A Review

Noor¹,

Daud²,

Ahmad³

et al. 2017

ijacsa

View full text Add to dashboard Cite

Abstract-Security component in IoT system are very crucial because the devices within the IoT system are exposed to numerous malicious attacks. Typical security components in IoT system performs authentication, authorization, message and content integrity check. Regarding authentication, it is normally performed using classical authentication scheme using crypto module. However, the utilization of the crypto module in IoT authentication is not feasible because of the distributed nature of the IoT system which complicates the message cipher and decipher process. Thus, the Physical Unclonable Function (PUF) is suggested to replace crypto module for IoT authentication because it only utilizes responses from set of challenges instead of cryptographic keys to authenticate devices. PUF can generate large number of challenge-response pairs (CRPs) which is good for authentication because the unpredictability is high. However, with the emergence of machine learning modeling, the CRPs now can be predicted through machine learning algorithms. Various defense mechanisms were proposed to counter machine learning modeling attacks (ML-MA). Although they were experimentally proven to be able to increase resiliency against ML-MA, they caused the generated responses to be instable and incurred high area overhead. Thus, there is a need to design the best defense mechanism which is not only resistant to ML-MA but also produces reliable responses and reduces area overhead. This paper presents an analysis on defense mechanisms against ML-MA on strong PUFs for IoT authentication.

show abstract