Tech Talk: Security Edition Using Machine Learning for Hunting Security Threats

Seeing the exponential hike in global cyber threat spectrum, organizations are now striving more for AI / Machine Learning based Analytics to supercharge threat detection and minimize the operational overheads of maintaining conventional static detection rules in large scale SOC. However, use of AI / Machine Learning in Security Operations is challenging due to the complex cyber security big data and numerous attacker techniques. In this webinar, Muath Saleh and Hafiz Farooq (from Saudi Aramco) shall explain how to use the analytical power of Splunk to hunt for cyber and insider threats, and also utilizes the Splunk Machine Learning Toolkit (MLKT) for novelty and outlier detection from the noisy security datasets. This webinar purviews Saudi Aramco’s experience of using Splunk for handling security big data, and explains amazing key capabilities for effective operational security procedures and threat hunting. In this session we discuss: Emerging Security Needs & Emerging Big Data Understanding the cyber security threat spectrum Cyber Security is a Big Data Best Practices for Handling Security Big Data Machine Learning & Modern SOC Supercharge Threat Detection with Algorithms Machine Learning Use Cases Optimal Machine Learning Workflow