Multidimensional Forensic Analysis of Onion Sites Using LSTM and Fuzzy Encoded Techniques: Insights and Applications in Digital Investigations

When it comes to digital investigations, the fact that onion sites make people anonymous makes investigative research much harder. For multidimensional forensic analysis of onion sites, this study suggests a new method that combines Long Short-Term Memory (LSTM) networks with fuzzy encoding methods. When you use the Tor network to get to onion sites, both users and services become anonymous, which makes it harder to do regular investigations. LSTM networks are used to look at trends in how people use the site and when material is updated over time. These networks are good at modeling sequential data. Fuzzy encoding methods work with LSTM to handle the uncertainty that comes with analyzing anonymous data, which makes investigative results more accurate. The method involves keeping time logs of what people do on onion sites and turning them into fuzzy sets to show how user interactions and content changes change over time. LSTM models are taught on these recorded sequences to find patterns that point to suspicious activity, like sudden changes in content or strange patterns of user access. This method can be used to find illegal activities, find secret services that host illegal content, and make profiles of how users behave in an anonymous network. Investigators can better track actions across onion sites when they use LSTM's ability to see how events depend on each other over time and fuzzy encoding's ability to deal with inaccurate data. Real-world onion site data used for experiments shows that the proposed method can find secret behaviors and patterns that are hard for traditional investigative methods to find. The results show that the accuracy and efficiency of detecting are much better than with traditional methods.