CWSOGG: Catching Web Shell Obfuscation Based on Genetic Algorithm and Generative Adversarial Network
Bibliographic record
Abstract
Abstract A web shell is a backdoor used by hackers to control Web servers and perform privilege escalation, and thus it is crucial to detect web shells effectively. However, the detection of obfuscated web shells has always been a challenge. Inspired by adversarial training methods in the field of computer vision, this paper proposes a generative adversarial network (GAN)-based web shell detection model training framework. Since there has been no method that can generate obfuscated web shells effectively, a generator based on the genetic algorithm, which combines and optimizes the pre-set obfuscation methods, is used to obtain new obfuscation combinations and generate obfuscated samples. The whole proposed framework is named the CWSOGG. When training the detection model, the generator generates web shells that can bypass the discriminator, and the discriminator catches the features of obfuscated samples. Through the adversarial training of the discriminator and generator, the detection model improves its ability to detect obfuscated web shells. To verify the proposed framework is flexible to different models, the discriminator based on four main neural networks has been implemented. Meanwhile, to build complete feature extraction models, both statistical and semantic features are extracted. Due to the lack of web shell data, a clean dataset containing 4,375 web shells is constructed and used to evaluate the CWSOGG. The results have shown that the detection accuracy of each model increases by 86.71% on the generated obfuscated web shells on average and by 7.50% on the simulated real-world obfuscated web shells on average.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".