Semi-Coherent Transmission Security for Low Power IoT Devices

McGinthy, Jason M.; Michaels, Alan J.

doi:10.1109/cybermatics_2018.2018.00059

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Attacks on the GPS Civilian/Acquisition (C/A) code are routinely taught using the Berlekamp-Massey algorithm [53]. Other attacks and protections have been devised for the use of PRNGs in semi-coherent processing, merging the efforts of deterministic cryptographic processing with noisy signal processing [58]. Additional statistical tests can also be expanded by maps involving the Kolmogorov-Sinai entropy of a system [59] to translate time sequences of outputs into conditional probabilities between output states.…”

Section: Introductionmentioning

confidence: 99%

“…Deliberately introducing noise in ciphertexts and classic cryptosystems such as DES has already been discussed in both the cases in which noise is added before (extrinsically) or after (intrinsically) the encryption process [62,63]. Intentionally injecting noise into PRNG systems specifically has been explored and implemented for a signal [58,64,65].…”

Section: Introductionmentioning

confidence: 99%

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Attacks and Defenses for Single-Stage Residue Number System PRNGs

Vennos

George

Michaels

2021

IoT

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This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attacks and Defenses for Single-Stage Residue Number System PRNGs

Vennos

George

Michaels

2021

IoT

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Secure Industrial Internet of Things Critical Infrastructure Node Design

McGinthy

Michaels

2019

IEEE Internet Things J.

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Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy Experimentation

Kolenbrander,

Husmann,

Henshaw

et al. 2024

JCP

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When personal information is shared across the Internet, we have limited confidence that the designated second party will safeguard it as we would prefer. Privacy policies offer insight into the best practices and intent of the organization, yet most are written so loosely that sharing with undefined third parties is to be anticipated. Tracking these sharing behaviors and identifying the source of unwanted content is exceedingly difficult when personal information is shared with multiple such second parties. This paper formulates a model for realistic fake identities, constructs a robust fake identity generator, and outlines management methods targeted towards online transactions (email, phone, text) that pass both cursory machine and human examination for use in personal privacy experimentation. This fake ID generator, combined with a custom account signup engine, are the core front-end components of our larger Use and Abuse of Personal Information system that performs one-time transactions that, similar to a cryptographic one-time pad, ensure that we can attribute the sharing back to the single one-time transaction and/or specific second party. The flexibility and richness of the fake IDs also serve as a foundational set of control variables for a wide range of social science research questions revolving around personal information. Collectively, these fake identity models address multiple inter-disciplinary areas of common interest and serve as a foundation for eliciting and quantifying personal information-sharing behaviors.

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Semi-Coherent Transmission Security for Low Power IoT Devices

Cited by 3 publications

References 24 publications

Attacks and Defenses for Single-Stage Residue Number System PRNGs

Attacks and Defenses for Single-Stage Residue Number System PRNGs

Secure Industrial Internet of Things Critical Infrastructure Node Design

Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy Experimentation

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