The Analysis of the Generator Architectures and Loss Functions in Improving the Stability of GANs Training towards Efficient Intrusion Detection
Bibliographic record
Abstract
Various research studies have been recently introduced in developing generative models, especially in computer vision and image classification. These models are inspired by a generator and discriminator network architecture in a min-max optimization game called Generative Adversarial Networks (GANs). However, GANs-based models suffer from training instability, which means high oscillations during the training, which provides inaccurate results. There are various causes beyond the instability behaviours, such as the adopted generator architecture, loss function, and distance metrics. In this paper, we focus on the impact of the generator architectures and the loss functions on the GANs training. We aim to provide a comparative assessment of various architectures focusing on ensemble and hybrid models and loss functions such as Focal loss, Binary Cross-Entropy and Mean Squared loss function. Experimental results on NSL-KDD and UNSW-NB15 datasets show that the ensemble models are more stable in terms of training and have higher intrusion detection rates. Additionally, the focal loss can improve the performance of detection minority classes. Using Mean squared loss improved the detection rate for discriminator, however with the Binary Cross entropy loss function, the deep features representation is improved and there is more stability in trends for all architectures.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".