Real-Time Detection of Cyber Threats via Dark Web Traffic Analysis Using Machine Learning and Deep Learning

Cyberattack strategies have advanced over the past few years, making it harder to defend against them even when countermeasures are employed. The ability to forecast cyberattacks, take adequate safeguards, and effectively use cyber intelligence that enables these activities is essential for handling this scenario successfully. This work will focus on the incoming packets from the dark web and propose an approach to extracting data that includes important information or intelligence from the dark web, characterizing each packet's information using machine learning, deep learning, and identifying upcoming threats. By utilizing an extreme gradient boosting architecture, we suggest a new dark net traffic analysis and network management framework to automate the detection of malicious content in real-time. Intelligent computer forensic tool to analyze network traffic and debug malware. Identification of encrypted and real-time traffic We provide a model that can carry out multiple tasks, like recognizing attacks and categorizing dark web packets, established on extreme gradient boosting decision trees and convolutional neural networks. The sophisticated solution is suggested to remove skill and effort barriers that prohibit many enterprises from properly securing their most crucial assets by automating the criminal intent dark net identification procedure. This strategy will enable us to comprehend the new cyber threats and, in the interim, to respond appropriately to hostile activity. This will enable us to advance in terms of security.