Security Hardening of Botnet Detectors Using Generative Adversarial Networks
Bibliographic record
Abstract
Machine learning (ML) based botnet detectors are no exception to traditional ML models when it comes to adversarial evasion attacks. The datasets used to train these models have also scarcity and imbalance issues. We propose a new technique named Botshot, based on generative adversarial networks (GANs) for addressing these issues and proactively making botnet detectors aware of adversarial evasions. Botshot is cost-effective as compared to the network emulation for botnet traffic data generation rendering the dedicated hardware resources unnecessary. First, we use the extended set of network flow and time-based features for three publicly available botnet datasets. Second, we utilize two GANs (vanilla, conditional) for generating realistic botnet traffic. We evaluate the generator performance using classifier two-sample test (C2ST) with 10-fold 70-30 train-test split and propose the use of 'recall' in contrast to 'accuracy' for proactively learning adversarial evasions. We then augment the train set with the generated data and test using the unchanged test set. Last, we compare our results with benchmark oversampling methods with augmentation of additional botnet traffic data in terms of average accuracy, precision, recall and F1 score over six different ML classifiers. The empirical results demonstrate the effectiveness of the GAN-based oversampling for learning in advance the adversarial evasion attacks on botnet detectors.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".