Bit Error Rate Analysis of Pre-formed ReRAM-based PUF

Jain, S. K.; Wilson, Taylor; Assiri, Sareh; Cambou, Bertrand

doi:10.1007/978-3-031-10467-1_54

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Reliability is gained by choosing cells for comparison that are previously known to have responses either reliably below or above the mean response. [21] While techniques such as noise injection are used in systems with powerful computing clusters, many IOT devices communicate with ordinary desktop computers or wireless routers. For these less powerful computers key fragmentation is used to simplify the RBC computation for the IOT server.…”

Section: Error Sources and Correction With Pufsmentioning

confidence: 99%

Security and robustness of AI-driven IOTs with differential sensing schemes

Heynssens,

Cambou,

Montano Claure

2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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Unexpected faulty sensors can seriously compromise the mission of strategic assets driven by artificial intelligence (AI), autonomous unmanned vehicles, and robots. In life threatening operation, these devices must make real time decisions based on the vital information collected from the sensors. AI is vulnerable to sensor failures caused both by malicious actions and by system breakdown. We propose a new scheme that is providing, in real-time, a unique fingerprint of a pair of differential sensors operating as a Physically Unclonable Function (PUF). The solution can be implemented with little computational complexity or power usage on most IOT devices driven by AI and a set of sensors. The differential circuitry ensures the authenticity of the source of the information generated from the sensors, thereby preventing malicious actors from deliberately falsifying sensor data. Without the fingerprint from the sensors, these actors will have reduced opportunities to access the server thus limiting this attack vector. Faulty sensors are identifiable since their erratic fingerprints differ from those identified at launch. The data originating from bad sensors is thereby ignored in favor of the data generated by redundant sensors when available in the system. This technology also ensures that the accuracy of the information from the sensor is within normal tolerances for a real-time AI environment. The fingerprints are used to create unique encryption keys for standardized post quantum cryptographic based communication. We are hereby demonstrating experimentally the relevance of the proposed differential circuits for these targeted applications.

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Section: Error Sources and Correction With Pufsmentioning

confidence: 99%

Security and robustness of AI-driven IOTs with differential sensing schemes

Heynssens,

Cambou,

Montano Claure

2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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“…By performing BER analysis we understand the reliability of the keys generated. 25 During key generation, lower BER is preferred because it enables the use of light weight ECC and yields low energy; 31 however, in this case, we wanted to increase BER significantly to be able to define the threshold error that can be tolerated during authentication. In order to calculate the BER, we calculate the mean of the errors and divide it by the length of the key generated.…”

Section: Bit Error Ratementioning

confidence: 99%

“…Figure 5. Ternary addressable public key infrastructure (TAPKI) protocol used to generate cryptographic keys using SRAM PUFs16,25, 29,30 …”

mentioning

confidence: 99%

Noise injection techniques in cryptographic keys to enhance security of autonomous systems

Jain

Cambou

2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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Cryptographic protocols are widely deployed to enhance the security of sensitive data shared across autonomous networks. Privacy and security issues are exacerbated due to increased vulnerability of the cryptographic key to various types of attack. It is extremely challenging to distribute cryptographic keys in contested areas on a real-time basis, therefore several protocols have been developed that use the fingerprints of electronic devices embedded in cyber physical systems for one-time use key generation. Various autonomous systems, including vehicles, robots, and industrial machines may be protected by these fingerprints, thus allowing the use of onetime encryption keys, thereby reducing the cybersecurity attack surface. To mitigate certain attacks and enhance security we propose methods to inject random noise in these keys, as well as the associated key recovery schemes.The benefit of noise injection is the ability to incrementally confuse the hacker while adding an extra layer of authenticity. The random percentage of noise injected in the key can only be identified and verified by an authorized party. An unexpected increase or decrease in the percentage of noise notifies the server that the autonomous system is potentially under attack. Experimentally, we demonstrate how noise levels up to 30% of the cryptographic keys can be injected, and how the temperature can alter the percentage of noise. We also quantify methods to increase the errors due to the instability of the fingerprints. Additionally, we review different strategies of noise injection and how we may leverage the injected noise for stronger authentication.

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