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 JP Vasseur presents at Tech Field Day Cisco Live Virtual Experience 2020 |
This Presentation date is November 20, 2020 at 08:00-09:00.
Presenters: JP Vasseur, Krishnan Thiruvengadam
Cisco makes networking smarter by applying advanced analytics and machine learning. Cisco AI endpoint analytics increases network visibility and security by applying these techniques to recognize and profile previously unidentified endpoints. In this live session, Cisco experts will dive into AI/ML technology and discuss how they are used to improve endpoint identification and grouping.
Speakers: Krishnan Thiruvengadam, Technical Marketing Engineer, and JP Vasseur (@jpvasseur), Cisco Fellow.
To learn more: Watch this brief video on network segmentation and read the white paper on AI endpoint analytics
Cisco’s AI/ML Networking Journey
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In this presentation, JP Vasseur PhD, Cisco Fellow and head of ML and Data Science engineering, presents the journey of Cisco’s AI applications from 2013 to 2020. This journey began with self-learning networks, then proceeded to AI network analytics for wireless automated detection and root cause analysis, and now to ML-based security classification and behavioral spoofing detection. He also demonstrates Cisco AI Endpoint Analytics on Cisco ISE.
Personnel: JP Vasseur
Advanced Endpoint Visibility with Cisco AI Endpoint Analytics
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Krishnan Thiruvengadam, Technical Marketing Engineer, discusses advanced endpoint visibility using Cisco’s AI Endpoint Analytics. After discussing the benefits and challenges of network visibility, Thiruvengadam presents Cisco’s next-generation endpoint visibility capability with AI-driven analytics and deep packet inspection. It provides high-fidelity profiling, improved classification, and better workflows with integration with third-party products.
Personnel: Krishnan Thiruvengadam
Cisco AL and ML for Endpoint Analytics and Detecting Spoofing Attacks
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JP Vasseur PhD, Cisco Fellow and head of ML and Data Science engineering, presents an advanced use case for machine learning in network security. He discusses clustering using machine learning and how Cisco can measure the efficacy of the results in terms of purity, percentage of unassigned, number of clusters, speed of unknowns resolution, and stability. He then gives a sneak peek at what comes next: Detecting spoofing attacks with ML/AI. ML is an excellent tool for detecting anomalies, and Vasseur presents a specific mechanism to accomplish this using ML instead of complex rules.
Personnel: JP Vasseur