Knowledge-Driven User Behavior Pattern Discovery for System Security Enhancement
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
Insider threads posed by authorized users have caused significant security and privacy risks to IT systems. The behavior of authorized users in using system services must be monitored and controlled. However, the administrators in large distributed systems are overwhelmed by the number of system users, the complexity and changing nature of user activities. This paper presents a new generation of intelligent decision support systems that effectively assist system administrators to get deep insight into the system users’ dynamic behavior patterns. With these patterns, the system administrators are capable of constructing dynamic refinement to the existing security policies. We explore the method of interactively and incrementally extracting user’s behavior patterns by combining data mining techniques with domain and system knowledge, and applying such knowledge to provide recommendations throughout the whole process. A prototype tool has been developed to analyze the audit logs from distributed medical imaging systems to validate the proposed approach.
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 | 0.001 |
| 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