How Machine Learning is Leveraged for Attack Detection Scenarios

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Presented by

Sanjay Raja, VP Product Marketing and Solutions | Antony Farrow, Sr Director of Solution Architecture

About this talk

Machine Learning (ML) is used to identify abnormal behavior and pinpoint malicious behavior. This webinar will show you how Gurucul uses adaptive and static ML models to identify two different MITRE attack stages: Lateral movement (T1110) and Valid/Default Account (T1078/001). In our demonstration, we'll run a basic model and identify an 'outlier' use case. During the webinar we'll address the following: • How ML models are used • How ML minimizes false positives • Why you can't rely on security alerts alone • Advantages of combining ML with security alerts Bring your questions!
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Gurucul is a security analytics company founded in data science that delivers radical clarity about cyber risk. Our REVEAL platform analyzes enterprise data at scale using machine learning and artificial intelligence. Instead of useless alerts, you get real-time, actionable information about true threats and their associated risk. The platform is open, flexible, cloud native and cost optimized. Organizations can save 50% or more while achieving complete data control, visibility, searchability, and analytics within a single console. Industry analysts have recognized our platform as a Visionary in the 2024 Gartner(R) Market Quadrant(TM) for SIEM for the third-consecutive year. Our solutions are used by Global 1000 enterprises and government agencies to minimize their cybersecurity risk. To learn more, visit Gurucul.com and follow us on LinkedIn and Twitter.