{'fraction_non_alphanumeric': 0.011579818031430935, 'fraction_numerical': 0.0, 'mean_word_length': 5.173469387755102, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Track Down Identity Leaks using Threat Intelligence

: Leakage of identity data is a precursor of identity theft in the Internet. Prevention measures are neither es-tablished to counteract identity theft nor is there any effective way to inform affected subjects after identity leakage has been discovered. To build an identity theft early warning system, it is crucial to ﬁnd evidence of identity leakage that happened in the past. News sites in the Internet regularly report about organizations suffering from data leakage. Those leaked data mostly contains member, customer or employee databases including private information. This paper presents a framework that automatically crawls and classiﬁes news articles with respect to identity data leakage. The framework is designed to monitor an arbitrary set of websites and to extract corresponding articles. The articles found are provided to analysts and security researchers with extracted information about the covered leaks. This lowers the amount of work that is necessary to stay up to date regarding leaks of identity data. The developed framework is a proof of concept and a foundation for further projects aiming to proactively warn affected users.