Developing a Software Agent to Access Drug-Related Information from the Dark Web
الملخص
Software agents have been increasingly utilized across various domains in information technology, including electronic commerce, information retrieval, and autonomous decision-making. In parallel, the dark web has become a prominent platform for illicit drug trading, particularly marijuana. While previous studies have focused on tracking users accessing the dark web, few have proposed structured methods for extracting drug-related information. Moreover, existing approaches are often time-consuming and complex, making them impractical for educational or analytical use. This research aims to model and develop a software agent capable of autonomously accessing and retrieving marijuana-related information from dark web marketplaces. The methodology follows a typical software agent development life cycle, encompassing requirement analysis, system design, development, evaluation, and documentation. Four specific plans were created to represent different dark web markets, each modeled using UML class diagrams. These diagrams were then normalized into a unified model to eliminate redundancy and streamline agent development. The results include a generalized UML class diagram that consolidates common functionalities such as user registration, login, drug search, and data retrieval across various markets. This normalized model facilitates more efficient and modular development of software agents in dark web environments. The main contribution of this study lies in its systematic modeling approach, offering a reusable framework for agent-based access to illicit online content for research and educational purposes. Future work will focus on extending this framework to support other domains and tasks within the dark web.
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