Hybrid Deep Learning Framework via Early Feature Fusion for XSS Attacks Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cross-site scripting (XSS) is still a significant security risk for online applications because it frequently avoids detection by using inventive payload formats and obfuscation.Most earlier studies either process statistical or sequential data alone or combine them at a later stage, which makes it difficult to capture their complementary interactions.This work differs from earlier approaches by integrating statistical and sequential representations at input stage, allowing both feature types to be learned jointly rather than fused later.Aiming to overcome this gap, this study suggests a hybrid deep learning framework that combines statistical features based on Term Frequency-Inverse Document Frequency (TF-IDF) with sequential dependencies learned through Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) networks using Early Feature Fusion.Principal Component Analysis (PCA) is used to reduce dimensionality in the TF-IDF dimension to enhance generalization.The proposed models have been evaluated on two well-known XSS datasets depending on eight key parameters.Both datasets, GRU and TF-IDF, performed well, with accuracy and F1-score exceeding 99%.The results indicate that early statistical and sequential feature fusion enhances the model's effectiveness in detecting malicious XSS payloads across the evaluated datasets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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