Web Crawler To Extract Links
DOI:
https://doi.org/10.48047/CU/54/02/2039-2051Keywords:
Web Crawler, URL Extraction, Domain Filtering, Rate Limiting, Performance OptimizationAbstract
Web crawlers are essential tools for automatically extracting URLs and other information from websites, but they often face
challenges when dealing with large and complex sites. This project focuses on creating a robust web crawler that efficiently
extracts URLs from a target domain and its subdomains, filters out external links, and allows users to filter links based on
HTTP status codes. The tool is designed to handle large websites through performance optimizations like rate limiting and
caching, ensuring it does not overload the websites it crawls. Implemented using Python and Bash, the crawler provides
flexible output options for ease of use, making it a versatile solution for web administrators, data analysts, and security
professionals. This paper discusses the design and implementation of the crawler, along with its performance-monitoring
features, ensuring accuracy and scalability in diverse web environments.
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References
. Kumar, R., & Sharma, V. (2021). Web Crawling and Data Mining: A Comprehensive Overview. Springer..
. Zhang, L., & Liu, Z. (2020). Machine learning techniques for improving web crawling efficiency. Journal of Computer Science, 38(4), 502-510.
https://doi.org/10.1016/j.jcse.2020.02.003.
. Singh, A., & Gupta, S. (2022). Performance optimization in web crawlers using caching and rate limiting. International Journal of Computer
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