Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process
The widespread use of web applications requires important changes in cybersecurity to protect online services and data. In the process of identifying security vulnerabilities in web applications, a systematic approach is employed to detect and mitigate cybersecurity risks. This approach utilizes web...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10719995/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850199140878778368 |
|---|---|
| author | Onur Aktas Ahmet Burak Can |
| author_facet | Onur Aktas Ahmet Burak Can |
| author_sort | Onur Aktas |
| collection | DOAJ |
| description | The widespread use of web applications requires important changes in cybersecurity to protect online services and data. In the process of identifying security vulnerabilities in web applications, a systematic approach is employed to detect and mitigate cybersecurity risks. This approach utilizes web crawlers to identify attack vectors. Traditional web crawling methods are resource-intensive and often need to be more efficient in handling dynamic JavaScript-rich content. Addressing this crucial gap, our study introduces an innovative approach to predict the necessity of JavaScript rendering, thereby enhancing the effectiveness and efficiency of security-oriented web crawlers. This approach seeks to reduce computational requirements and quicken the security evaluation process through the use of machine learning algorithms. By utilizing a dataset containing the source code from the main pages of 17,160 websites, our experimental results demonstrate a 20% reduction in execution time compared to full JavaScript rendering, indicating an improvement in resource usage without any significant reduction in coverage. Our methodology significantly improves the efficiency of security-focused web crawlers and helps security scanners to detect security risks of web applications with fewer resources. |
| format | Article |
| id | doaj-art-6d3a32cb1bab4f07bedd1f77fc6964f9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6d3a32cb1bab4f07bedd1f77fc6964f92025-08-20T02:12:41ZengIEEEIEEE Access2169-35362024-01-011216168816169610.1109/ACCESS.2024.348164610719995Making JavaScript Render Decisions to Optimize Security-Oriented Crawler ProcessOnur Aktas0https://orcid.org/0009-0000-9144-6494Ahmet Burak Can1https://orcid.org/0000-0002-0101-6878S4E, Ankara, TürkiyeDepartment of Computer Engineering, Hacettepe University, Ankara, TürkiyeThe widespread use of web applications requires important changes in cybersecurity to protect online services and data. In the process of identifying security vulnerabilities in web applications, a systematic approach is employed to detect and mitigate cybersecurity risks. This approach utilizes web crawlers to identify attack vectors. Traditional web crawling methods are resource-intensive and often need to be more efficient in handling dynamic JavaScript-rich content. Addressing this crucial gap, our study introduces an innovative approach to predict the necessity of JavaScript rendering, thereby enhancing the effectiveness and efficiency of security-oriented web crawlers. This approach seeks to reduce computational requirements and quicken the security evaluation process through the use of machine learning algorithms. By utilizing a dataset containing the source code from the main pages of 17,160 websites, our experimental results demonstrate a 20% reduction in execution time compared to full JavaScript rendering, indicating an improvement in resource usage without any significant reduction in coverage. Our methodology significantly improves the efficiency of security-focused web crawlers and helps security scanners to detect security risks of web applications with fewer resources.https://ieeexplore.ieee.org/document/10719995/Crawlercyber securityJavaScriptmachine learningrenderingweb application security |
| spellingShingle | Onur Aktas Ahmet Burak Can Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process IEEE Access Crawler cyber security JavaScript machine learning rendering web application security |
| title | Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process |
| title_full | Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process |
| title_fullStr | Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process |
| title_full_unstemmed | Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process |
| title_short | Making JavaScript Render Decisions to Optimize Security-Oriented Crawler Process |
| title_sort | making javascript render decisions to optimize security oriented crawler process |
| topic | Crawler cyber security JavaScript machine learning rendering web application security |
| url | https://ieeexplore.ieee.org/document/10719995/ |
| work_keys_str_mv | AT onuraktas makingjavascriptrenderdecisionstooptimizesecurityorientedcrawlerprocess AT ahmetburakcan makingjavascriptrenderdecisionstooptimizesecurityorientedcrawlerprocess |