Real-time alert correlation using stream data mining techniques
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
With the large volume of alerts produced by low-level detectors, management of intrusion alerts is becoming more challenging. Alert Correlation addresses this issue by providing a condensed, yet more useful view of the network from the intrusion standpoint. In this paper, we propose a new framework for real-time alert correlation that incorporates novel techniques for aggregating alerts into structured patterns and incremental mining of frequent structured patterns. In the proposed framework, time-sensitive statistical relationships between alerts are maintained in an efficient data structure and are updated incrementally to reflect the latest trends of patterns. The results of experiments with synthetic and real-world datasets demonstrate the efficiency of the proposed techniques. Our Frequent Structure Mining algorithm scales linearly with the size of the dataset and the proposed framework can cope with the throughput of a large-scale network. The ability to answer time-sensitive queries about patterns is another advantage of this work compared to other methods.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it