Optimizing Web Scraping for Efficiency with NLP and Machine Learning

Web scraping is a powerful mechanism for large-scale data collection from online sources, particularly in cybersecurity contexts. However, conventional scraping approaches suffer from slow extraction speeds, high resource consumption, and susceptibility to detection. This paper presents an optimized framework that integrates Natural Language Processing (NLP) and Machine Learning (ML) to enhance web scraping efficiency. Through analysis of existing literature, we investigate strategies for automating data extraction, improving threat classification, and refining cybersecurity analysis. Real-world applications, technical challenges, and prospective directions are examined, with the aim of advancing smarter and more effective cybersecurity data frameworks.