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@article{206910,
        author = {Indushree S M and Shwetha K R and J. Chandrashekhara},
        title = {An Intelligent Data Analytics Framework for Modelling, Understanding, and Predicting Cybercrime Underground Economies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3257-3263},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206910},
        abstract = {The growth of digital technologies has been matched by a rise in cybercrime, much of it enabled by underground online marketplaces where criminals trade malware, stolen data, and hacking tools—often via the dark web and cryptocurrencies, which make such activity hard to trace. This study proposes a data analytics approach to understanding underground cybercrime activity using machine learning. Publicly available cybersecurity datasets will be preprocessed (cleaning, deduplication, handling missing values) and analyzed using a Naïve Bayes classifier to identify predictable patterns in cybercrime activity. Model performance will be evaluated using precision, recall, F1-score, and a confusion matrix, followed by a graphical analysis of underground market trends and attack patterns. The findings aim to give cybersecurity analysts, researchers, and policymakers a cost-effective, data-driven framework for detecting and anticipating emerging cybercrime trends, ultimately supporting stronger global cybersecurity practices.},
        keywords = {Cybercrime, Data Analytics, Machine Learning, Underground Economy, Naïve Bayes, Threat Intelligence, Predictive Analytics},
        month = {July},
        }