Post 4: Training a Variational Autoencoder for Anomaly Detection Welcome to Part 4 of my series on building an intelligent log analysis system. In Part 3, I focused on parsing raw logs into a structured format for machine learning. Today, I will explore how our system uses a Variational Autoencoder Read more…
In this part of this series on building a smarter log analysis system. In this post, we’ll focus on the crucial step of parsing logs for machine learning. This step transforms raw log files into a structured format that can be processed by our VAE (Variational Autoencoder) for anomaly detection. Read more…
Quick Definitions Frequently Asked Questions Q1: Why use a VAE for anomaly detection? VAEs are well-suited for learning patterns in high-dimensional data. By compressing data into a latent space, they can reconstruct normal patterns well but struggle with anomalous data, making anomalies easy to detect. Q2: What happens if the Read more…
Introduction In the first post of this series, I looked into the idea of using machine learning to detect anomalies in server logs and the benefits it might bring.In this post, I’ll guide you through setting up a Python-based environment for building and running our anomaly detection system. This includes Read more…
Introduction Like many system administrators, I’ve relied on pflogsumm to keep tabs on my email server through daily summary reports. While these automated emails have served me well, I’ve always wondered if there was more to learn from this data. This curiosity sparked a journey into artificial intelligence, Python development, Read more…