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UID:pretalx-hack-lu-2024-AEV77X@pretalx.com
DTSTART;TZID=CET:20241022T143000
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DESCRIPTION:In the current digital landscape\, organizations often do not b
 ecome aware immediately when their data is compromised and sold online. Ou
 r objective is to minimize the duration between the exposure of data on th
 e internet and its detection by the public. The dark web serves as a prima
 ry marketplace for the trade of personal information\, accessible safely o
 nly through the use of the Tor browser. This paper focuses on monitoring s
 ignificant trading forums on the dark web and demonstrates the method of w
 eb scraping specifically designed for dark web sites. Utilizing data harve
 sted from these sites\, we have trained a BERT classification model to cat
 egorize transaction posts into five distinct types of data leaks\, enablin
 g rapid identification of the leak type associated with each post.\n\nFurt
 her\, we employ the Retrieval-Augmented Generation (RAG) technique to vect
 orize dark web data\, maintaining privacy while leveraging mainstream larg
 e language models to address concerns pertinent to cybersecurity analysts.
  This approach allows researchers to analyze dark web data effectively. Ul
 timately\, the data collected from the dark web is formatted into STIX (St
 ructured Threat Information Expression) and integrated into the MISP (Malw
 are Information Sharing Platform) system to automate the generation of Cyb
 er Threat Intelligence (CTI) reports. This methodology not only enhances t
 he timeliness and accuracy of threat detection but also contributes to mor
 e efficient and proactive cybersecurity management.
DTSTAMP:20260612T052803Z
LOCATION:Europe - Main Room
SUMMARY:Automating Dark Web CTI Reports ​ with RAG Insight for MISP Shari
 ng - Shing-Li Hung
URL:https://pretalx.com/hack-lu-2024/talk/AEV77X/
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