Combating Insider Threats by User Profiling from Activity Logging Data
Bibliographic record
Abstract
In cybersecurity, malicious insider threats represent a huge issue for organizations and may pose the greatest threat category. Combating the insider risks need an understanding of the behavior of each insider. Markov chains (MC) are particularly well suited to model behaviors from network traffic, they were extensively used for modeling and clustering actions. In this article, we explore Markov process to model profiles for individual users rather than modeling actions. That is, for every set of actions, there is a Markov chain labeled by that action flow that specifies the state transition probabilities resulting from each unique user. This modeling is appropriate to add a temporal component to data stream clustering, and its static nature can be dynamically adapted to each user's profile. From the network traffic, we demonstrate that potential insider threats can be pointed by formulating the request associated to a given threat scenario in form of a sequence of actions and scoring it against each user's pre-established MC model.
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How this classification was reachedexpand
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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".