Detecting Network Anomalies Using Different Wavelet Basis Functions
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
Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we present a novel network anomaly detection approach based on wavelet analysis, approximate autoregressive and outlier detection techniques. In order to characterize network traffic behaviors, we proposed fifteen features and applied them as the input signals in our wavelet-based approach. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive comparison for four different typical wavelet basis functions on detecting network intrusions. Our work aims to unveil a question when applying wavelet techniques for detecting network attacks, that is "do wavelet basis functions have an important impact on the intrusion detection performance?". Moreover, to the best of our knowledge, the work is the first to analyze the 1999 DARPA's network traffic using flow data instead of its original raw packet data.
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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 | 0.001 | 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