An Algorithm for the Initial Detection of Malicious Traffic Based on the Autoencoder Reconstruction Error and a Variational Model: the Influence of the Error Distribution Density on the Performance Indicators of the Models
Bibliographic record
Abstract
The emergence of new sophisticated types of attacks forces the community of computer security researchers to constantly improve detection tools and response methods. The present study explores different factors of autoencoders and variational models that influence their effectiveness in identifying novel attack types and malicious network traffic. The general idea of the proposed algorithm is to construct a confidence interval for the reconstruction error of the training sample, based on which a decision is made on the maliciousness of a particular traffic. Additional emphasis was placed on selecting an appropriate error metric to minimize the overlap between the density distributions of reconstruction errors for normal and malicious traffic. In the study of the variational model, the effect of the t-distribution on the quality of detecting new types of attacks was investigated. The studies were conducted on the CIC-IDS2017 dataset of the Canadian Cybersecurity Institute, containing up to 14 types of traffic and attacks. The experimental results show that with a competent selection of the error measure and the threshold values of the confidence interval, our models outperform existing analogues in various performance indicators.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".