A Framework to Optimize Deep Learning based Web Attack Detection Using Attacker Categorization
Bibliographic record
Abstract
The outstanding usage of web applications across the globe has enabled people to access desired information online with a few clicks. This has also enabled skilled attackers to compromise the availability, integrity and confidentiality of the data and information available on these websites. This paper proposes a framework for detecting and obstructing a large number of web attacks and scanning probes based on features of an HTTP (Hyper Text Transfer Protocol) request packet and also caters for POST HTTP data. We first trained four traditional machine learning models i.e. Decision Tree, Support Vector Machine (SVM), Naive Bayesian and Linear Regression by using a well-known publicly available dataset. It was found out that Decision Tree outperforms the rest in terms of performance and accuracy. Finally, a Convolutional Neural Network (CNN) based deep learning approach was implemented and tested on a well-known publicly available dataset. It results in optimal performance and an accuracy of 99.94%. The deep learning approach was enhanced by the introduction of a User Categorization Feature which uses cookies to categorise malicious attackers.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".