Biblio

In this information age, web crawling on the internet is a prime source for data collection. And with the surface web already being dominated by giants like Google and Microsoft, much attention has been on the Dark Web. While research on crawling approaches is generally available, a considerable gap is present for URL extraction on the dark web. With most literature using the regular expressions methodology or built-in parsers, the problem with these methods is the higher number of false positives generated with the Dark Web, which makes the crawler less efficient. This paper proposes the dedicated parsers methodology for extracting URLs from the dark web, which when compared proves to be better than the regular expression methodology. Factors that make link harvesting on the Dark Web a challenge are discussed in the paper.

The dark web searching mechanism is unlike surface web searching. On one popular dark web, Tor dark web, the search is often directed by directory like services such as Hidden Wiki. The numerous dark web data collection mechanisms are discussed and implemented via crawling. The dark web crawler assumes seed link, i.e. hidden service from where the crawling begins. One such popular Tor directory service is Hidden Wiki. Most of the hidden services listed on the Hidden Wiki 2020 page became unreachable with the recent upgrade in the Tor version. The Hidden Wiki 2021 page has a limited listing of services compared to the Hidden Wiki 2020 page. This motivated authors of the present work to establish the role of Hidden wiki service in dark web research and proposed the hypothesis that the dark web could be reached better through customized harvested links than Hidden Wiki-like service. The work collects unique hidden services/ onion links using the opensource crawler TorBot and runs similarity analysis on collected pages to map to corresponding categories.

Various Machine Learning techniques are considered to be "black-boxes" because of their limited interpretability and explainability. This cannot be afforded, especially in the domain of Cyber-Physical Systems, where there can be huge losses of infrastructure of industries and Governments. Supervisory Control And Data Acquisition (SCADA) systems need to detect and be protected from cyber-attacks. Thus, we need to adopt approaches that make the system secure, can explain predictions made by model, and interpret the model in a human-understandable format. Recently, Autoencoders have shown great success in attack detection in SCADA systems. Numerous interpretable machine learning techniques are developed to help us explain and interpret models. The work presented here is a novel approach to use techniques like Local Interpretable Model-Agnostic Explanations (LIME) and Layer-wise Relevance Propagation (LRP) for interpretation of Autoencoder networks trained on a Gas Pipelines Control System to detect attacks in the system.