August 29, 2019

Detecting malicious traffic with machine learning

machine learning

By Paul Rigor, Ph.D., Research Scientist, and Harkeerat Bedi, Ph.D., Research Scientist

The Future of Threat Management

Machine learning offers a unique opportunity to automate security research and stay ahead of application security threats. In this article, we’ll share our research into applying neural networks on production traffic data, as well as ideas for future applications. By applying predictive analysis techniques to incoming requests to our platform, we’re developing new ways to detect, analyze, and mitigate malicious traffic.

Incorporating the output of the machine learning algorithms into our WAF product allows us to create a more robust threat detection system, enabling more accurate detection of malicious traffic toward our platform. Coupled with the flexibility of a Dual WAF platform, we can quickly incorporate these insights directly into a production configuration for a customer’s live traffic to help mitigate threats. Machine learning helps us develop agile threat detection and mitigation systems that are ready to grow to respond to emerging threats.

Check out the full article on Medium now.

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