Using Neuro-Fuzzy Approach to Reduce False Positive Alerts
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
One of the major problems of Intrusion Detection Systems (IDS) at the present is the high rate of false alerts that the systems produce. These alerts cause problems to human analysts to repeatedly and intensively analyze the false alerts to initiate appropriate actions. We demonstrate the advantages of using a hybrid neuro-fuzzy approach to reduce the number of false alarms. The neuro-fuzzy approach was experimented with different background knowledge sets in DARPA 1999 network traffic dataset. The approach was evaluated and compared with RIPPER algorithm. The results shows that the neuro- fuzzy approach significantly reduces the number of false alarms more than the RIPPER algorithm and requires less background knowledge sets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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