CVAE-AN: Atypical Attack Flow Detection Using Incremental Adversarial Learning
Why this work is in the frame
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Bibliographic record
Abstract
Network Intrusion Detection Systems (NIDS) are powerful tools for identifying and deterring cybersecurity attacks nowadays. However, while these modern IDS can detect typical attacks, recent studies show their poor performances in identifying unknown or dynamically changing atypical attacks. Another issue with the training aspect of such systems is the problem of class imbalance which impedes their performance, especially for minority attack classes. This renders IDS systems vulnerable to both adversarial as well as non-AI synthesized atypical attacks when deployed in a real network. To reduce misclassification (especially for minority classes) and detect atypical attack flows, we propose a novel adversarial incremental learning approach based on a hybrid model consisting of a Conditional Variational Autoencoder (CVAE) and a Generative Adversarial Network (GAN) namely, CVAE-Adversarial Network (CVAE-AN). The binary IDS has been trained using the CICIDS2017 dataset and evaluated using multiple atypical attacks. Simulation results demonstrate that the proposed technique significantly improves the performance of the IDS against different atypical attacks and outperforms the state-of-the-art detection models as well as class balancing methods.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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