Darkintellect: An Approach to Detect Cyber Threat Using Machine Learning Techniques on Open-Source Information

Abstract

Advances in information technology have led to a significant increase in cybercrime, security challenges, intruders and hackers. Cyberspace has a wealth of data that cybersecurity experts may utilize to develop threat intelligence, which will eventually aid in the prevention of cyberattacks and the protection of a company's network infrastructure. In contrast to the traditional random method of attack, cyber-attacks are now planned and carried out in a sophisticated manner targeting a specific target group, which is safe for the vast majority of netizens who have a keen awareness of the vast resources in cyberspace. The volume of Cyber Security literature available disseminated using social networking websites, particularly Twitter, has surged in recent days. A deep analysis of this data can aid in the development of a cyber threat situational awareness framework. We need scalable and efficient technology that can identify and summarize the information needed for a particular large data stream.

To Identify text linked to cyber threats, this paper recommends leveraging publicly available information from the Darknet platforms and Surface Web. With around 87 percent accuracy, our methodology can give law enforcement authorities and information security analyst with credible information that can be used to design control and prevention measures for cyber-attacks. We use machine learning techniques to assess the different sorts of online threats on social media in this research work. We discussed the algorithm based on the f-measurement value compared to accuracy and precision score.