Exploring the Dark Web for Cyber Threat Intelligence

Cyber attack strategies have become more prominent in recent years, making it more difficult to stop an assault, even if some form of countermeasure is used. Even with protection systems in place, it is challenging to totally avoid all cyber intrusions. We are, in a sense, in a defense-only posture. A strategy for exploiting this threat will be provided, which will combine social network analysis with cognitive computing to filter hostile behavior and forecast their vulnerabilities. The major goal is to harvest crucial postings from dark web forums, including posts from malicious hackers, and disseminate information like virus trading and hacking strategies. Hence ,We are proposing an approach to extract forums which include important information or intelligence and identify traits of each forum using AI and ML Techniques. Information gathering from the dark-web communities is quite a challenging task, data cannot address real-world cybersecurity problems considering the importance of information gathering. The dataset containing forum posts from the darknet were extracted from different websites using different web crawlers .Then the retrieved data is cleaned , normalized, parsed, processed and classified and labeled according to their severity. After this phase different ML algorithms were applied on labeled data . Finally the model is tested with the inputs and used for predicting the cyber threats for future. The main focus of this research will assist us in identifying future developing risks in cyberspace and high propensity measures to counter such malicious tasks. With recent technological advancements, recent research has revealed that dark-web indications may be connected with event data and leveraged to forecast intrusions, indicating perhaps a more threat-focused cybercrime is on the horizon.