Defense Mechanism Vulnerability Analysis of Ring Oscillator PUFs Against Neural Network Modeling Attacks using the Dragonfly Algorithm

Oun, Ahmed; Niamat, Mohammed

doi:10.1109/eit48999.2020.9208320

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…There are a few research works where NNs have been used to analyze the vulnerabilities of cryptosystems. The use of feed-forward NNs FNNs has been applied to the vulnerability analysis of Physical Unclonable Functions (PUFs) [48]. More specifically FNNs are being used to model out attack scenarios against the Challenge Response Pairs (CRPs) of the PUFs [48].…”

Section: Vulnerability Analysismentioning

confidence: 99%

“…The use of feed-forward NNs FNNs has been applied to the vulnerability analysis of Physical Unclonable Functions (PUFs) [48]. More specifically FNNs are being used to model out attack scenarios against the Challenge Response Pairs (CRPs) of the PUFs [48]. It was found that given very few CRPs as a baseline, an FNN using the Dragonfly Algorithm(DA) was able to accurately predict more challenge-response pairs with a 85.2% accuracy attacking the Configurable Ring Oscillator PUF and 71.3% against the XOR-inverter Ring Oscillator PUF [48].…”

Section: Vulnerability Analysismentioning

confidence: 99%

“…More specifically FNNs are being used to model out attack scenarios against the Challenge Response Pairs (CRPs) of the PUFs [48]. It was found that given very few CRPs as a baseline, an FNN using the Dragonfly Algorithm(DA) was able to accurately predict more challenge-response pairs with a 85.2% accuracy attacking the Configurable Ring Oscillator PUF and 71.3% against the XOR-inverter Ring Oscillator PUF [48]. DA works by moving neurons as dragonflies, moving them closer to the goal (food sources) and away from bad predictions (enemies) by using the neurons as dragonflies on a dimensional grid with speed and velocity [48].…”

Section: Vulnerability Analysismentioning

confidence: 99%

See 2 more Smart Citations

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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Section: Vulnerability Analysismentioning

confidence: 99%

Section: Vulnerability Analysismentioning

confidence: 99%

Section: Vulnerability Analysismentioning

confidence: 99%

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The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

View full text Add to dashboard Cite

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“…In our earlier work [43], we presented an analytical study of the vulnerability of the Configurable Ring Oscillator PUF and the XOR-Inverter Ring Oscillator PUF against Feed-Forward Neural Network (FNN) attacks using the Dragonfly Algorithm. That limited study showed that both designs are vulnerable to this type of attack.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Swarm Intelligence Based ANN Algorithms for Attacking PUFs

2021

Self Cite

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Physical Unclonable Functions (PUFs) are used for authentication and generation of secure cryptographic keys. However, recent research work has shown that PUFs, in general, are vulnerable to machine learning modeling attacks. From a subset of Challenge-Response Pairs (CRPs), the remaining CRPs can be effectively predicted using different machine learning algorithms. In this work, Artificial Neural Networks (ANNs) using swarm intelligence-based modeling attacks are used against different silicon-based PUFs to test their resiliency to these attacks. Amongst the swarm intelligence algorithms, the Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Particle Swarm Optimizer (PSO) and the Grey Wolf Optimizer (GWO) are used. The attacks are extensively performed on six different types of PUFs; namely, Configurable Ring Oscillator, Inverter Ring Oscillator, XOR-Inverter Ring Oscillator, Arbiter, Modified XOR-Inverter Ring Oscillator, and Hybrid Delay Based PUF. From the results, it can be concluded that the first four PUFs under study are vulnerable to ANN swarm intelligence-based models, and their responses can be predicted with an average accuracy of 71.1% to 88.3 % for the different models. However, for the Hybrid Delay Based PUF and the Modified XOR-Inverter Ring Oscillator PUF, which are especially designed to thwart machine learning attacks, the prediction accuracy is much lower and in the range of 9.8 % to 14.5 %.

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