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 transforming enterprise security with user behavior based machine learning and predictive analytics. Using identity to monitor for threats, Gurucul provides Actionable Risk Intelligence™ to protect against targeted and under-the-radar attacks. Gurucul is able to proactively detect, prevent, and deter advanced insider threats, fraud and external threats to system accounts and devices using self-learning, behavioral anomaly detection algorithms. Gurucul is backed by an advisory board comprised of Fortune 500 CISOs, and world renowned-experts in government intelligence and cyber security. The company was founded by seasoned entrepreneurs with a proven track record of introducing industry changing enterprise security solutions. Our mission is to help organizations protect their intellectual property, regulated information, and brand reputation from insider threats and sophisticated external intrusions.