Investigation of Ensemble Learning Approach for Early Detection of Traffic from Tor Browser

In this research paper, ensemble learning is used to create a new paradigm for network security. This study's main goal is to detect and prevent malicious communication from entering network setups, in the campus area network The dark web, often known as the “dark net,” is an encrypted portion of the internet that cannot be accessed without specific permission or settings and is not included in search engine results. People that need privacy do soon the dark web for totally lawful reasons, despite the fact that it is often depicted as a space utilized by criminal groups, such as the transmission of secret company information or communication by political activists. In order to secure user identity and ensure anonymity, the dark net encryption technology transmits users' data via a huge number of intermediary servers. The Tor Browser is a web browser created with the goal of increasing online privacy and anonymity. It is based on the Tor network, a decentralized system of volunteer-run servers that routes internet infrastructure across several nodes to obfuscate the user's IP address and make it challenging to monitor their online activity. It may not provide full security against all dangers. The study in this area enhances network security and safety via the application of advanced machine learning. Through the detection of possible flaws and trend analysis of Tor use, this study aims to improve network security. Several different machine learning methods are combined to assess the intricate network traffic patterns. With the ability to quickly identify deviations from the standard, the computer can identify fake data and facilitate proactive intervention. The great degree of precision of ensemble learning demonstrated its efficacy. The network security techniques that might be employed to protect open networks from attacks are improved by this study.