Randa S. Basheer

Work place: Syrian Virtual University, Damascus, Syria

E-mail: randa_82880@svuonline.org

Website:

Research Interests: World Wide Web

Biography

Randa S. Basheer is a web sciences master student at Syrian Virtual University. She has a Bachelor degree in information technology engineering, Damascus University, Damascus, Syria, 2006. Ms. Basheer co-authored a published article with Dr. Alkhatib: “Crawling the Dark Web: A Conceptual Perspective, Challenges and Implementation”, Journal of Digital Information Management (JDIM), April 2019. Her current research of interest is the Dark Web.

Author Articles
Mining the Dark Web: A Novel Approach for Placing a Dark Website under Investigation

By Bassel Alkhatib Randa S. Basheer

DOI: https://doi.org/10.5815/ijmecs.2019.10.01, Pub. Date: 8 Oct. 2019

In the last two decades, illicit activities have dramatically increased on the Dark Web. Every year, Dark Web witnesses establishing new markets, in which administrators, vendors, and consumers aim to illegal acquisition and consumption. On the other hand, this rapid growth makes it quite difficult for law and security agencies to detect and investigate all those activities with manual analyses. In this paper, we introduce our approach of utilizing data mining techniques to produce useful patterns from a dark web market contents. We start from a brief description of the methodology on which the research stands, then we present the system modules that perform three basic missions: crawling and extracting the entire market data, data pre-processing, and data mining. The data mining methods include generating Association Rules from products’ titles, and from the generated rules, we infer conceptual compositions vendors use when promoting their products. Clustering is the second mining aspect, where the system clusters vendors and products. From the generated clusters, we discuss the common characteristics among clustered objects, find the Top Vendors, and analyze products promoted by the latter, in addition to the most viewed and sold items on the market. Overall, this approach helps in placing a dark website under investigation.

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