Biblio

Traditional security controls, such as firewalls, anti-virus and IDS, are ill-equipped to help IT security and response teams keep pace with the rapid evolution of the cyber threat landscape. Cyber Threat Intelligence (CTI) can help remediate this problem by exploiting non-traditional information sources, such as hacker forums and "dark-web" social platforms. Security and response teams can use the collected intelligence to identify emerging threats. Unfortunately, when manual analysis is used to extract CTI from non-traditional sources, it is a time consuming, error-prone and resource intensive process. We address these issues by using a hybrid Machine Learning model that automatically searches through hacker forum posts, identifies the posts that are most relevant to cyber security and then clusters the relevant posts into estimations of the topics that the hackers are discussing. The first (identification) stage uses Support Vector Machines and the second (clustering) stage uses Latent Dirichlet Allocation. We tested our model, using data from an actual hacker forum, to automatically extract information about various threats such as leaked credentials, malicious proxy servers, malware that evades AV detection, etc. The results demonstrate our method is an effective means for quickly extracting relevant and actionable intelligence that can be integrated with traditional security controls to increase their effectiveness.

Hacker forums and other social platforms may contain vital information about cyber security threats. But using manual analysis to extract relevant threat information from these sources is a time consuming and error-prone process that requires a significant allocation of resources. In this paper, we explore the potential of Machine Learning methods to rapidly sift through hacker forums for relevant threat intelligence. Utilizing text data from a real hacker forum, we compared the text classification performance of Convolutional Neural Network methods against more traditional Machine Learning approaches. We found that traditional machine learning methods, such as Support Vector Machines, can yield high levels of performance that are on par with Convolutional Neural Network algorithms.