Two-Steps Insider Threat Detection Protocol (2S-ITDP) Based on Computer Usage Pattern Analysis
Bibliographic record
Abstract
The server, a computer system logging on of the organization's clients, has to verify whether the log-on requester is the formals registered before accessing.Unfortunately, the common log-on protocol rarely detected the imposter requester and untrusted organizational clients.This paper presents the proactive protocol to prevent the outside intruder and detect untrusted corporate clients.This research innovates the step by step practical control flow of whole activities of proactive and preventive framework about intrusion.The suggested protocol comprises the proposed authentication protocol and the client's computer system using sequential pattern analysis.These two activities can inhibit outside intruders, and detect and eliminate the fake client (known other organizational clients' secret passwords).Otherwise, it can see whether the real clients are working correctly as prior computer usage.The evaluation presents that the "two-step insider threat detection protocol (2S-ITDP)" can proactively prevent both outside and inside intruders better than the typical authentication protocol.Furthermore, the accuracy of classification is about eighty-eight percent.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".