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Record W3010117922

Electromagnetic Fault Injection On Two Microcontrollers: Methodology, Fault Model, Attack and Countermeasures

2020· dissertation· en· W3010117922 on OpenAlexfundno aff
Haohao Liao

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsMicrocontrollerFault (geology)Fault injectionComputer scienceEmbedded systemReliability engineeringComputer securityEngineeringGeologyOperating systemSeismologySoftware
DOInot available

Abstract

fetched live from OpenAlex

Cryptographic algorithms are being applied to various kinds of embedded devices such as credit card, smart phone, etc. Those cryptographic algorithms are designed to be resistant to mathematical analysis, however, passive Side Channel Attack (SCA) was demonstrated to be a serious security concern for embedded systems. These attacks analyzed the relationship between the side channel leakages (such as the execution time or power consumption) and the cryptographic operations in order to retrieve the secret information. Various countermeasures were proposed to thwart passive SCA by hiding this relationship. \n \nAnother different type of SCA, known as the active SCA is Fault Injection Attack (FIA). FIA can be divided into two phases. The first one is the fault injection phase where the attacker aims at injecting a fault to a target circuit with a specific timing and spatial accuracy. The second phase is the fault exploitation phase where the attacker exploits the induced fault and forms an attack. The major targets for the fault exploitation phase are the cryptographic algorithms and the application-sensitive processes. Over the last one and a half decades, FIA has attracted expanding research attention. \n \nThere are various techniques which could be used to conduct an FIA such as laser, Electromagnetic (EM) pulse, voltage/clock glitch, etc. EM FIA achieves a moderate spatial resolution and a high timing resolution. Moreover, since the EM pulse can pass through the package of the chip, the chip does not need to be fully decapsulated to run the attack. However, there remains a lack of understanding of the fault injected to the cryptographic devices and the countermeasures to protect them. Therefore, it is important to conduct in-depth research on EM FIA. \n \nThis dissertation concentrates on the study of EM FIA by analyzing the experimental results on two different devices, PIC16F687 and LPC1114. The PIC16F687 applies a two-stage pipeline with a Harvard structure. Faults injected to the PIC16F687 resulted in instruction replacement faults. After analysis of detailed experiments, two new Advanced Encryption Standard (AES)-128 attacks were proposed and empirically verified using a two-step attack approach. These new AES attacks were proposed with lower computational complexity unlike previous Differential Fault Analysis (DFA) algorithms. Instruction specific countermeasures were designed and verified empirically for AES to prevent known attacks and provide fault tolerant protection. \n \nThe second target chip was the LPC1114, which utilizes an ARM Cortex-M0 core with a three-stage pipeline and a Von Neumann structure. Fault injection on multiple LDR instructions were analyzed indicating both address faults and data faults were found. Moreover, the induced faults were investigated with detailed timing analysis taking the pipeline stall stage into consideration. Fault tolerant countermeasures were also proposed and verified empirically unlike previous fault tolerant countermeasures which were designed only for the instruction skip fault. \n \nBased on empirical results, the charge-based fault model was proposed as a new fault model. It utilizes the critical charge concept from single event upset and takes the supply voltage and the clock frequency of the target microcontroller into consideration. Unlike previous research where researchers suggested that the EM pulse induced delay or perturbation to the chip, the new fault model has been empirically verified on both PIC16F687 and LPC1114 over several frequencies and supply voltages. \n \nThis research contributes to state of the art in EM FIA research field by providing further advances in how to inject the fault, how to analyze the fault, how to build an attack with the fault, and how to mitigate the fault. This research is important for improving resilience and countermeasures for fault injection attacks for secure embedded microcontrollers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.276
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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