Practical Feedback on Using Machine Learning in Information Security

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Practical Feedback on Using Machine Learning in Information Security
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Discover how AI/ML and information security teams combat bad actors using strategies like IP/User/Token-based rate limiting, CAPTCHA challenges, and more.

Have you ever wondered about how X identifies bots that are tweeting spam? or how banks identify fraudulent accounts? or how GitHub identifies faulty servers in the network? Such systems are built by information security teams that monitor and take down such activities at scale using AI/ML systems. In cases where the automation can’t handle or identify, incident response teams take them down. These learnings are captured and then train new ML classifiers to identify outliers.

IP-Based Rate Limiting Limits the number of requests from a single IP address within a specific timeframe. Helps mitigate DDoS attacks and brute-force attempts. IP-Based Rate Limiting Limits the number of requests from a single IP address within a specific timeframe. Helps mitigate DDoS attacks and brute-force attempts. Limits the number of requests from a single IP address within a specific timeframe. Limits the number of requests from a single IP address within a specific timeframe.

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