A Comprehensive Review of Website Content Filtering Algorithms: Techniques, Challenges, and Future Directions

Abstract

The exponential growth in web content fueled an increased demand for smart, scalable and responsible filters for website content. This talk explores the origins, classification, and specifications comparison (traditional rule based and keyword based system to modern machine learning based algorithm and hybrid attempts) of the content filtering algorithm. Particular attention is paid to their working methods, the evaluation processes, deployment complexities as well as their action in practical deployments. The article outlines how static filters are on the verge of becoming entirely inadequate for combating dynamic and encrypted threats and it describes how more recent innovations - visual phishing detection, context aware systems and federated learning - are changing the landscape of filtering paradigm. This chapter draws on the latest 30 academic works on literature on the theoretical concepts and direct practices of content filtering systems and compare them along the axis of accuracy, precision, recall, and scalability. Besides, it identifies major challenges including privacy compromise, false positives, and the need for explainable AI. The review ends with the directions for future research, base on personalization, transparency, and balloon of the deep learning architectures. Through this comprehensive study the rearing provides useful inferences to researchers, cybersecurity professionals; and platform developers to develop safer and more diligent web spaces.