Content area
Abstract
The Dark Web is an ever-growing phenomenon that has not been deeply explored. It is no secret that in recent years, malware has become a powerful threat to technology users. The Dark Web is known for supporting anonymity and secure connections for private interactions. Over the years, it has become a rich environment for displaying trends, details, and indicators of emerging malware threats. Through the application of data science and open-source intelligence techniques, trends in malware distribution can be studied. In this research, we create a framework for helping identify malware threat distribution patterns. We examine this type of Dark Web activity by utilizing an automated and manual approach for collecting data on malware exchanges. Furthermore, a comparative analysis is conducted to determine which approach is more effective and efficient. Our framework for identifying current or future malware threats that are distributed on the Dark Web is refined by examining the weaknesses and strengths of each gathering approach.
