Presented at
BSidesLV 2016,
Aug. 3, 2016, 10:35 a.m.
(55 minutes)
Current threat detection technologies lack the ability to present an accurate and complete picture of how threats are executed and fail to put together the multi contextual relationship of exploit chain indicators. A combination of behavioral and machine learning technologies can provide a more effective and complete assessment and prevention of threats in organizations relying on dispersed, static single indicator technologies. This approach also makes use of current static and single threat indicator technologies using Big Data computational models.
Presenters:
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Rod Soto
- Director of Security Research - JASK.AI
Rod Soto has over 15 years of experience in information technology and security. He is currently the director
of security research at JASK.AI. He has spoken at ISSA, ISC2, OWASP, DEFCON, BlackHat, RSA, Hackmiami, BSides and also been
featured in Rolling Stone Magazine, Pentest Magazine, Univision and CNN. Rod Soto was the winner of the 2012
BlackHat Las Vegas CTF competition and is the founder and lead developer of the Kommand && KonTroll competitive hacking
Tournament series.
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Joseph Zadeh
- Senior Data Scientist - Splunk Inc.
Joseph Zadeh studied mathematics in college and received a BS from University California, Riverside and an MS and PhD from Purdue University. While in college, he worked in a Network Operation Center focused on security and network performance baselines and during that time he spoke at DEFCON and Torcon security conferences. Most recently he joined Caspida as a security data scientist. Previously, Joseph was part of the data science consulting team at Greenplum/Pivotal helping focused on Cyber Security analytics and also part of Kaiser Permanentes first Cyber Security R&D team.
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