Massive distributed and parallel log analysis for organizational security
Xiaokui Shu, John Smiy, Danfeng Daphne Yao, Heshan Lin · 2013
Security log analysis is extremely useful for uncovering intrusions and anomalies. However, the sheer volume of log data demands new frameworks and techniques of computing and security. We present a lightweight distributed and parallel security log analysis framework that allows organizations to analyze a massive number of system, network, and transaction logs efficiently and scalably. Different from the general distributed frameworks, e.g., MapReduce, our framework is specifically designed for security log analysis. It features a minimum set of necessary properties, such as dynamic task scheduling for streaming logs. For prototyping, we implement our framework in Amazon cloud environments (EC2 and S3) with a basic analysis application. Our evaluation demonstrates the effectiveness of our design and shows the potential of our cloud-based distributed framework in large-scale log analysis scenarios.