Deep Learning: How AI Threat Detection Works

Hands-on workshop for developers and AI professionals, on a topic gaining a lot of attention recently. Register here.

Recording and GitHub material will be available to registrants who cannot attend the free 60-min session.

Summary

Come join our upcoming webinar “Real-Time AI Threat Detection Using Kafka” explore how Kafka, combined with cutting-edge AI technologies, can be wielded to detect and counter cybersecurity threats as they occur.

Our session will include a hands-on demo and code-share where we'll utilize a deep learning model and similarity search to identify and classify network intrusion traffic. The demonstration will be grounded on a dataset of labeled traffic events which are then transformed into vector embeddings to represent network traffic events comprehensively. These embeddings will serve as a linchpin for measuring similarity between different network events, aiding in the identification and classification of benign and malicious events.

What You’ll Learn:

• How to set up Kafka for real-time data processing and threat detection.
• Applying SingleStoreDB's similarity search for identifying infrequent occurrences in network traffic.
• Utilizing a deep learning model to transform network traffic data into vector embeddings for effective similarity measurement.
• Hands-on approach to constructing a network intrusion detection system that can classify events as benign or malicious.

Register here.