On Security Best Practices, Systematic Analysis of Security Advice, and Internet of Things Devices
Bibliographic record
Abstract
While Internet of Things (IoT) security best practices have recently attracted considerable attention from industry and governments, academic research has highlighted the failure of many IoT product manufacturers to follow accepted practices.We begin by investigating a surprising lack of consensus, and void in the literature, on what (generically) best practice means, and provide a technical examination of related terminology.We use iterative inducting coding to design an analysis methodology for categorizing security advice and measuring its actionability.We use this methodology to analyze three datasets: a set of 1013 IoT security best practices, recommendations, and guidelines, and two formally recommended IoT security advice documents.We find all three sets to be largely non-actionable.Through design and use of this methodology, we identify the characteristics of actionable security advice.We also analyze recent work on IoT device identification based on three identification objectives (distinguish device instances, distinguish device classes, and authenticate device identity), and the technical approaches by which they are reached: device fingerprinting, classification, and authentication.We differentiate the role of these objectives and approaches in IoT security, and develop a model relating them.viii I offer a personal anecdote highlighting an important formative moment in my PhD timeline, for which I am grateful.After the initial few years of my PhD studies, I was struggling to find a research direction that was unique and worthy of indepth study, but also an appropriate fit for my background.One day during one of our weekly meetings, seeing that I was struggling, Paul explained his idea for a general research direction based on a line from a paper that we had both read, but I had overlooked at the time.I found the research direction-IoT security advice-interesting, unique, and it seemed appropriate for my background.I am ever grateful for Paul's suggestion, not only as it eventually led to the unique line of research culminating in this thesis, but that he first allowed me to struggle on my own (with guidance, of course) before intervening with a strong nudge in the right direction.I believe this, and all the other lessons he has taught me, has made me a better researcher.Additionally, I would like to thank the members of the Carleton Security Research Labs (CCSL and CISL) for their support and guidance over the years.In particular, I'd like to thank my close colleague Hemant Gupta, who has been both supportive and helpful since I first joined the research lab; and Dr. David Barrera for assisting with portions of the research in this thesis, and general guidance in navigating the challenges of a PhD program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".