Securing Web Applications with Secure Coding Practices and Integrity Verification
Bibliographic record
Abstract
The concept of security in web applications is not new. However, it is often ignored in the development stages of the applications. Being multitiered and spread across different domains, it is challenging to come up with a security solution that works for all web applications. Moreover, developers are more inclined to implement features and often do not practice secure coding. Therefore, countless web applications are launched with security vulnerabilities like cross-site scripting, injection attacks and resource alterations. In addition, code tampering on the client side is a serious security risk for web applications. In our opinion, integrating security features should be a part of the development process. Without practicing secure coding and having an integrity verification system in place, it is difficult to defend security attacks. In this paper, we present a system that helps developers to implement security measures on the client side code based on the best practices of secure coding. We also develop an integrity verification module to prevent code tampering attacks on the client side. The proposed approach can be integrated with both new and existing web applications. We implement our approach for a number of JavaScript-based applications and the results show that our approach increased the security of the applications and prevented any modifications performed on the client side.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".