Detection of Denial of Service Attacks in Communication Networks
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
Detection of evolving cyber attacks is a challenging task for conventional network intrusion detection techniques. Various supervised machine learning algorithms have been implemented in network intrusion detection systems. However, traditional algorithms require long training time and have high computational complexity. Therefore, we propose detection of denial of service cyber attacks in communication networks by employing the broad learning system (BLS) that requires shorter training time while achieving comparable performance. Because designing effective detection systems relies on training and test datasets that contain anomalous network traffic data, in this paper we evaluate the performance of various BLS models by using recently generated network intrusion datasets. The best accuracy and F-Score were often achieved using BLS with cascades while BLS with incremental learning usually required shorter training time.
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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