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UID:pretalx-bsides-london-2024-7GAQNS@cfp.securitybsides.org.uk
DTSTART;TZID=GMT:20241214T100000
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DESCRIPTION:Our talk introduces an innovative framework for automating the 
 identification and handling of malware samples targeting web servers\, lev
 eraging big data analytics and machine learning to cluster and track activ
 e malware campaigns. We will demonstrate an innovative and unique framewor
 k that employs heuristic analysis to autonomously identify and process web
 -delivered malware samples. This framework enhances the efficiency and acc
 uracy of malware detection in large data sets\, reducing the reliance on m
 anual intervention\, and enabling near real-time threat hunting\, and camp
 aign tracking. \n\nBuilding upon the collected malware data\, we utilize b
 ig data analytics techniques to track and monitor malwares\, cluster simil
 ar malware samples and associated network activity\, to unveil patterns an
 d connections between various campaigns. This clustering approach provides
  deeper insights into the tactics\, techniques\, and procedures (TTPs) emp
 loyed by threat actors\, facilitating the identification of overarching st
 rategies and objectives. \n\nWe will conclude with a detailed analysis of 
 notable real-world malware campaigns identified through this system. Atten
 dees will gain insights into the operational methodologies of these campai
 gns\, their impact and the defensive measures that can be employed. Case s
 tudies will highlight real-world applications and the effectiveness of our
  automated approach in enhancing cybersecurity posture.
DTSTAMP:20260417T050719Z
LOCATION:Track 2
SUMMARY:Unmasking APT Malware Activity: Real-World Malware Campaign Trackin
 g Using Big Data Analytics and Machine Learning Clustering - Daniel Johnst
 on\, Ori Nakar
URL:https://cfp.securitybsides.org.uk/bsides-london-2024/talk/7GAQNS/
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