How I Built a Real-Time Anomaly Detection Engine for a Cloud Storage Platform
The article describes the development of a real-time anomaly detection system for a cloud storage platform, designed to identify and block malicious traffic by analyzing Nginx logs. The system uses Python to monitor request rates with sliding windows, calculates dynamic baselines to detect deviations, and automatically blocks suspicious IPs using iptables. It also includes features like adaptive thresholds for error surges, a backoff-based banning schedule, and Slack alerts for operational visibility. The entire setup runs in Docker alongside Nextcloud without direct integration.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3903364) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } babaolu Posted on Apr 29 How I Built a Real-Time Anomaly Detection Engine for a Cloud Storage Platform #beginners #machinelearning #security #showdev Introduction Imagine you're running a cloud storage platform — thousands of users uploading files, downloading documents, sharing links — all day, every day. Now imagine a hacker decides to hammer your server with thousands of fake requests per second.
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