CTO of Interset
Stephan Jou is CTO of Interset, which uses machine learning and behavioral analytics to provide unprecedented insight into how corporate intellectual property is being attacked, moved, shared and utilized. Interset has a strategic investment and technology development agreement with In-Q-Tel (IQT), the strategic investor that identifies innovative technology solutions to support the missions of the U.S. Intelligence Community.
Jou was previously a Technical Architect, Research Staff Member and Sr. Manager at IBM’s Business Analytics Office of the CTO. In his career at Cognos and IBM, he architected and lead the development of over ten 1.0 Cognos and IBM products in the areas of cloud computing, mobile, visualization, semantic search, data mining and neural networks. A frequent speaker, he has presented at a wide range of security and industry specific conferences including Information Security Forum, Intel Security focus, ISACA, CSO50, SINET, Gartner Security and Risk Management, Cyber Security for Critical Assets USA Summit, as well as contributed to the Verizon Data Breach Investigation Report.
With rapidly growing volumes of data and better behavioral monitoring and machine learning that leverages new data sources and big data, security is poised to achieve major breakthroughs in accurate insider and targeted outsider threat detection. This presentation will cover what security professionals need to know about:
• How to use behavior analytics, machine learning and math to get real-time and predictive visibility into attack vectors that are penetrating perimeter defenses in attempts to gain control over machines and accounts.
• What types of insight can be gained into applications, machines, accounts, and critical data as it is being targeted.
• How to use this information to stop attacks as well as deliver better correlate forensic data for incident investigation time and cost.
Subject: Machine Learning–a Primer for Security
Learning outcome from presentation
Attendees will learn about real world use cases at five different organizations including a global manufacturer, defense contractor, life science company, and pharmaceutical and media companies. Detailed explanations of the types of attacks uncovered will be given, as well as the feature engineering, mathematical models, visualizations, development techniques and open source tools that are being used in these real world implementations.