Bibliographic record
Abstract
This paper presents an online anomaly detection system capable of handling operational network traffic of large networks (such as an ISP). We also aim at an effective practical anomaly diagnosis to collect actionable intelligence enabling an automated response. To achieve these objectives, we use the following approach: (1) We model the network status as a stream of tensors where each cell models a time series in the network. (2) We detect anomalous tensors at time steps by using an unsupervised tensor representation learning model. (3) We produce actionable intelligence by diagnosis of anomaly detection results and by identifying the abnormal time series that are most likely the causes of each anomaly in the tensor, and (4) we further analyze the traffic corresponding to the anomalous time-series by an innovative method to extract and isolate the attack traffic. (5) We provide solutions for the challenges of streaming data anomaly detection such as large volume, high velocity, seasonality, and concept drift. We apply our approach to the complete test set of UGR data to show its practicality and effectiveness. Not only can we detect and isolate most of the labeled attack traffic, but we also identify many organic attack activities in the UGR data. We report our results on the complete UGR dataset that shows high detection and isolation rate for labelled attacks in the dataset. We also report some of the organic attacks detected (labeled as background in the dataset). Our analysis shows that the isolated background traffic represent interesting and potentially malicious behaviour and can provide invaluable insight for cyber-threat researchers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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".