Information Extraction from Dark Web Contents and Logs
Atif Ali, Muhammad Qasim · 2023
This chapter focuses on extracting meaningful information from unstructured data, which makes up a large portion of the deep web. Background: Traditional analysis methods failed to analyze the dark web’s unstructured content. This chapter examined how natural language processing (NLP) tools analyze web content. Human-like text software for synthesis, as a result, can link data sets that appear to be unconnected. Web content analysis, deep web usage, and web structure have been explored. In addition, policy requirements for harvesting and analyzing unstructured content from the deep web are discussed in this chapter. Because the information kept on the dark web is supposed to be concealed from public access, extracting and analyzing it may have legal implications. This chapter examines the risks and mitigations of handling deep web data. This chapter examined powerful systems for analyzing large amounts of data, including big data and log analysis tools. Structured, semi-structured, and unstructured data all make up big data. As a result, big data tools can deal with unstructured data. The process of analyzing unstructured data has been clearly outlined. Finally, the chapter examined unstructured web content using text analytics. The NLP-based tool can analyze large amounts of data for the deep web. The following chapter will look at deep web forensics and the various methods used to perform forensics on the deep web.