Using open-source intelligence and machine learning to analyze cyberattack trends in the African cyberspace

Abstract

Cyber-attacks continue to evolve, circumventing modern security solutions and exploiting discovered software vulnerabilities. Businesses and governments tend to respond in a reactive manner when dealing with these attacks, mostly due to limited situational awareness, lack of threat intelligence sharing, and lack of timely and actionable threat intelligence. The African cyberspace is vulnerable of cyber threats and risks that require timely monitoring to enable decision makers to understand the cybersecurity threat landscape on continuous basis. This paper, therefore, aims to use open-source intelligence from social media and machine learning techniques to analyze the emerging cyber-attack trends in Africa and demonstrate the value of such information for situational awareness that could be used by decision makers to mitigate cyber threats and risks. The paper contributes strategic and technical guidelines for exploiting OSINT to timely respond to cyber-attacks.