Cloning Physically Unclonable Functions

Helfmeier, Clemens; Boit, Christian; Nedospasov, Dmitry; Seifert, Jean-Pierre

doi:10.1109/hst.2013.6581556

Cited by 234 publications

(116 citation statements)

References 16 publications

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“…Other costs include damage to reputation, toxic waste cleanup, product returns, lost product cycles as in the case of the Samsung Galaxy 7 (possibly due to adding the "feature" of rapid battery charging [34]), refunds, and staff for customer support, etc. So far the cost to suppliers and users of hijacked IoT devices is near zero, so the crash rate is very high.…”

Section: Resultsmentioning

confidence: 99%

Internet of Things Behavioral-Economic Security Design, Actors & Cyber War

Shuler¹,

Smith²

2017

AIT

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Using security incident history we identify threats to and using the IoT and other ubiquitous devices emerging since 2012, gaining widespread recognition in 2016, and only lightly addressed in either IoT security literature or the press. We show the IoT has likely already been used in cyber war between major powers. The new threats, most notably "hijack," are larger than previous threats combined, but only mildly affect suppliers, and only a few clients. Using a successful behavioral-economic model we show that traditional mitigation places responsibility on un-affected parties and likely will not work. For suppliers, there are profit-conflicted motives, as the new threat rides on a profit vehicle. The new threat circumvents conventional security architecture at a behavioral level. We analyze each actor-target pair and evaluate technical strategies. More effective technical strategies are suggested where old ones are overmatched by the budgets, technical prowess or regulatory power of hostile actors, or the technical nature of the threats. Consolidated action may be needed, but regulation is difficult because of conflicts of interest within the national security community.

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Section: Resultsmentioning

confidence: 99%

Internet of Things Behavioral-Economic Security Design, Actors & Cyber War

Shuler¹,

Smith²

2017

AIT

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“…Tarnovsky [51] demonstrated the vulnerability of the algorithms and circuits that are utilized to process the PUF responses to invasive attacks through micro-probing the logic blocks, registers and the bus transfer of PUF devices. As for SRAM PUFs, they are susceptible to extreme operating conditions [52] and physical tampering [53]. As a result, the SRAM PUF hardware can be physically inspected and modified [54].…”

Section: ) Attacks On Weak Pufsmentioning

confidence: 99%

“…As a result, the SRAM PUF hardware can be physically inspected and modified [54]. Furthermore, it was established by Helfmeier et al [53] that upon gaining the response of an SRAM PUF, C 1 , a focus ion 132 | P a g e www.ijacsa.thesai.org beam (FIB) can be utilized to alter the circuits of SRAM PUF, C 2 so that C 2 will have a very similar challenge/response behavior as C 1 .…”

Section: ) Attacks On Weak Pufsmentioning

confidence: 99%

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

Noor¹,

Daud²,

Ahmad³

et al. 2017

ijacsa

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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.

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“…They became a vibrant field of research during the last years and promised to be "a means of building secure smartcards" due to their assumed resistance to physical attacks [76]. Very recently, however, it was shown that SRAM PUFs are not unclonable [88] and the same setup that we used for the photonic side channel attacks was used to prove that all arbiter PUFs can be completely and linearly characterized by means of PEA [166].…”

Section: Advice For Cryptographersmentioning

confidence: 99%

Why cryptography should not rely on physical attack complexity

Krämer

2016

It - Information Technology

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Ich versichere an Eides statt, dass ich diese Dissertation selbständig verfasst und nur die angegebenen Quellen und Hilfsmittel verwendet habe. Datum Für meine Eltern AbstractEver since the first side channel attacks and fault attacks on cryptographic devices were introduced in the mid-nineties, new possibilities of physical attacks have been consistently explored. The risk that these attacks pose is reduced by reacting to known attacks and by developing and implementing countermeasures against them. For physical attacks whose theory is known but which have not been conducted yet, however, the situation is different. Attacks whose physical realization is assumed to be very complex are taken less seriously. The trust that these attacks will not be realized due to their physical complexity means that no countermeasures are developed at all. This leads to unprotected devices once the assessment of the complexity turns out to be wrong.This thesis presents two practical physical attacks whose theory is known for several years. Since neither attack has previously been successfully implemented in practice, however, they were not considered a serious threat. Their physical attack complexity has been overestimated and the implied security threat has been underestimated. First, we introduce the photonic side channel, which offers not only temporal resolution, but also the highest possible spatial resolution. Due to the high cost of its first realization, it has not been taken seriously. We show both simple and differential photonic side channel analyses. Then, we present a fault attack against pairing-based cryptography. Due to the need for at least two independent precise faults in a single pairing computation, it has also not been taken seriously. We show how attackers can reveal the secret key of symmetric as well as asymmetric cryptographic algorithms based on these physical attacks. We present countermeasures on the software and the hardware level, which help to prevent these attacks in the future.Based on these two presented attacks, this thesis shows that the assessment of physical attack complexity is error-prone. Hence, cryptography should not rely on it. Cryptographic technologies have to be protected against all physical attacks, have they already been realized or not. The development of countermeasures does not require the successful execution of an attack but can already be carried out as soon as the principle of a side channel or a fault attack is understood.

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Cloning Physically Unclonable Functions

Cited by 234 publications

References 16 publications

Internet of Things Behavioral-Economic Security Design, Actors & Cyber War

Internet of Things Behavioral-Economic Security Design, Actors & Cyber War

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

Why cryptography should not rely on physical attack complexity

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Cloning Physically Unclonable Functions

Cited by 234 publications

References 16 publications

Internet of Things Behavioral-Economic Security Design, Actors &amp; Cyber War

Internet of Things Behavioral-Economic Security Design, Actors &amp; Cyber War

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

Why cryptography should not rely on physical attack complexity

Contact Info

Product

Resources

About

Internet of Things Behavioral-Economic Security Design, Actors & Cyber War

Internet of Things Behavioral-Economic Security Design, Actors & Cyber War