Employee Surveillance Based on Free Text Detection of Keystroke Dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, many studies have highlighted the unprecedented growth in security threats from multiple and varied sources faced by corporate, as well as governmental organizations. People inside the organization with ready access to confidential or proprietary data can easily violate the organization security policy, maliciously or inadvertently, without being caught. In order to protect their reputation and valuable assets, many organizations take the dramatic but necessary step of deploying and operating employee surveillance and monitoring tools within their network perimeters. In this chapter, we discuss employee surveillance schemes from both technological and legal perspectives. We argue that keystroke dynamics could be used to fight effectively against insider threat, and as such it could play an important role in employee surveillance. We present a keystroke recognition scheme based on free text detection that goes beyond the traditional approach of using keystroke dynamics for authentication or employee performance evaluation, and consider using such information for dynamic user profiling. The generated profiles can be used to identify reliably perpetrators in the event of security breach. Such form of user profiling provides a very effective way of combating insider threat that is less intrusive to individual privacy.
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.
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