On Security Best Practices, Systematic Analysis of Security Advice, and Internet of Things Devices
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,065 | 0,179 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,015 | 0,019 |
| Études des sciences et des technologies | 0,005 | 0,018 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».