AI Web-Contents Analyzer for Monitoring Underground Marketplace

Abstract

It is well known that products for cyber-attacks such as exploits and malware codes are illegally traded on hidden web services called Dark Web that are not indexed by conventional search engines we usually use. In general, it is not easy to capture the whole picture of trade activities on Dark Web because special browsers and tools are needed to visit such dark market sites and forums. And they usually require us to make a registration and/or to pass a qualification test. However, to understand the trends of cyber-attacks, there is no doubt that Dark Web is one of the useful information sources. In this paper, we try to understand the sales trends of illegal products for cyber-attacks from the largest marketplace called AlphaBay, which is relatively easier to collect information without passing any qualification tests, To monitor business trades on Dark Web, we develop an AI web-contents analyzer, which consists of a Tor crawler to collect the product information and a topic analyzer to capture the trends of what people are interested in and popular products of cyber-attacks. For this purpose, we use a topic model called Latent Dirichlet Allocation (LDA) and we show that the topic analysis would be helpful for predicting new cyber-attacks.