Evaluation of Deep Learning in Detecting Unknown Network Attacks
Bibliographic record
Abstract
Deep Learning (DL) provides powerful solutions for detecting network attacks by attempting to discover patterns of abnormal traffic in the network. Previous studies have demonstrated the effectiveness of DL in detecting attacks with known profiles, i.e. attack patterns with which DL-based methods have been trained. However, their performance against unknown attacks or attacks with dynamically changing profiles have not been comprehensively examined. Given the increasing sophistication of cyberattacks on network-based resources, it is crucial to understand how DL-based methods would perform in such scenarios and to what extent they can handle deviation from their training models. In this paper, we focus specifically on the performance of two commonly proposed DL-based techniques, DNN and LSTM, for binary prediction of unknown DoS and DDoS attacks. We train these models using the benchmark CICIDS2017 dataset, and then we generate a new test dataset in a simulated environment to measure the performance of the proposed models. We also demonstrate that retraining the models on a dataset with new unknown attacks improves the True Positive Rate (TPR) by 99.8% and 99.9% for DNN and LSTM respectively.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".