Identification of Data Breaches from Public Forums

Adversaries initiate their cyberattacks towards different entities such as healthcare or business institutes, and a successful attack causes data breaches. They publish their success stories in public forums for ranking purposes. The victim entities can be informed early about the data breach event if these forums are analyzed properly. Though few studies already focused on this sector, their data sets and codes are not public. Most importantly, the sources of their data sets do not exist today, which makes their novelty unclear and unreliable. To address and handle the above concerns, this study reinvestigates this domain with Machine Learning, Ensemble Learning, and Deep Learning. A web crawler is developed for downloading the dataset from the public forum of Nulled website. Feature extraction is done using TF-IDF and GloVe. Performance analysis showed that SVM achieved at most 90.80% accuracy with linear kernel. Implementations are published with a GitHub link.