Bibliographic record
Abstract
Possession of modern smartphones is becoming increasingly ubiquitous, and with this rise in usage comes a rise in the amount of sensitive data being stored on them. Despite this, the high-frequency, low-duration nature of the average smartphone session makes passwords or PIN-locks even less usable than in the desktop context. To combat these issues, implicit authentication (IA) schemes can be developed and deployed to smartphones. IA schemes continuously authenticate users by profiling their behaviour using the variety of sensors prevalent on the phones, such as touchscreens and accelerometers. When a non-owner acquires the device and attempts to access sensitive data on it, the IA scheme recognizes the difference in behaviour and automatically ejects the attacker from the system. In particularly sensitive contexts, IA schemes can also be deployed as a secondary defence mechanism on top of explicit authentication, providing layered security in the event of, for example, a shoulder-surfing attack compromising the device's PIN or an operating system vulnerability allowing its bypass. In this work, we evaluate existing proposals for IA schemes using different behavioural feature sets, and evaluate them against real-world data to show when they are (and are not) useful. We have implemented them in an easily extensible open source framework for the Android operating system called Itus, which allows other researchers to iteratively improve on the existing mechanisms for performing IA. Itus performs IA at the app level, which we have shown allows app developers to selectively protect sensitive data while decreasing the impact on battery life and device performance, and at the same time obtaining better detection accuracy for the IA scheme being invoked.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.629 | 0.419 |
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".