A Behavior Profiling Model for User Authentication in IoT Networks based on App Usage Patterns
Why this work is in the frame
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Bibliographic record
Abstract
Access to Internet of Things (IoT) devices is, in most cases, achieved remotely through end-user devices such as smartphones. However, these devices are susceptible to theft or loss, and their use by unauthorized users could lead to unauthorized access to IoT networks, consequently allowing access to user information. Due to the inherent weaknesses in many authentication approaches, such as knowledge-based authentication, as well as the complications involved in employing them for continuous and implicit authentication, focus has turned to a consideration of behavioral-based authentication. As most access to IoT devices is achieved through end-user devices, a variety of information can be extracted and utilized for continuous authentication without requiring further user intervention. As an example, the ability to continuously retrieve application usage profiles and sensor data on such devices strengthens the argument for employing behavioral-based mechanisms for continuous user authentication. Behavioral techniques that are user-friendly and non-intrusive can be utilized in the background to continuously and transparently verify users. This paper discusses behavioral-based authentication mechanisms with regard to security and usability. It then presents an authentication model that verifies users with an average F-measure of 96.5%. Overall, the preliminary results are promising and show the effectiveness and usability of the proposed model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it