Deep-Learning-Based Security Evaluation on Authentication Systems Using Arbiter PUF and Its Variants
| Autor: | Mitsugu Iwamoto, Takanori Machida, Kazuo Sakiyama, Risa Yashiro |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Secure authentication
Authentication Computer science business.industry Deep learning Physical unclonable function Arbiter 02 engineering and technology Computer security computer.software_genre 020202 computer hardware & architecture Support vector machine ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS Core (game theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Vulnerability (computing) Computer network |
| Zdroj: | Advances in Information and Computer Security ISBN: 9783319445236 IWSEC |
| DOI: | 10.1007/978-3-319-44524-3_16 |
| Popis: | Fake integrated circuit (IC) chips are in circulation on the market, which is considered a serious threat in the era of the Internet of Things (IoTs). A physically unclonable function (PUF) is expected to be a fundamental technique to separate the fake IC chips from genuine ones. Recently, the arbiter PUF (APUF) and its variants are intensively researched aiming at using for a secure authentication system. However, vulnerability of APUFs against machine-learning attacks was reported. Upon the situation, the double arbiter PUF (DAPUF), which has a tolerance against support vector machine (SVM)-based machine-learning attacks, was proposed as another variant of APUF in 2014. In this paper, we perform a security evaluation for authentication systems using APUF and its variants against Deep-learning (DL)-based attacks. DL has attracted attention as a machine-learning method that produces better results than SVM in various research fields. Based on the experimental results, we show that these DAPUFs could be used as a core primitive in a secure authentication system if setting an appropriate threshold to distinguish a legitimate IC tags from fake ones. |
| Databáze: | OpenAIRE |
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