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SUMMARY:Unmasking APT Malware Activity: Real-World Malware Campaign Tracki
 ng Using Big Data Analytics and Machine Learning Clustering - Daniel Johns
 ton\, Ori Nakar
DTSTART;TZID=Europe/London:20241214T100000
DTEND;TZID=Europe/London:20241214T104500
DTSTAMP:20260916T084754Z
UID:pretalx-bsides-london-2024-7GAQNS@cfp.securitybsides.org.uk
DESCRIPTION:Our talk introduces an innovative framework for automating the
  identification and handling of malware samples targeting web servers\, le
 veraging big data analytics and machine learning to cluster and track acti
 ve malware campaigns. We will demonstrate an innovative and unique framewo
 rk that employs heuristic analysis to autonomously identify and process we
 b-delivered malware samples. This framework enhances the efficiency and ac
 curacy of malware detection in large data sets\, reducing the reliance on 
 manual intervention\, and enabling near real-time threat hunting\, and cam
 paign tracking. \n\nBuilding upon the collected malware data\, we utilize 
 big data analytics techniques to track and monitor malwares\, cluster simi
 lar malware samples and associated network activity\, to unveil patterns a
 nd connections between various campaigns. This clustering approach provide
 s deeper insights into the tactics\, techniques\, and procedures (TTPs) em
 ployed by threat actors\, facilitating the identification of overarching s
 trategies and objectives. \n\nWe will conclude with a detailed analysis of
  notable real-world malware campaigns identified through this system. Atte
 ndees will gain insights into the operational methodologies of these campa
 igns\, their impact and the defensive measures that can be employed. Case 
 studies will highlight real-world applications and the effectiveness of ou
 r automated approach in enhancing cybersecurity posture.
LOCATION:Track 2
URL:https://cfp.securitybsides.org.uk/bsides-london-2024/talk/7GAQNS/
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