An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis

Microservice architecture (MSA) is a new software architecture, which divides a large single application and service into dozens of supporting microservices. With the increasingly popularity of MSA, the security issues of MSA get a lot of attention. In this paper, we propose an algorithm for mining causality and the root cause. Our algorithm consists of two parts: invocation chain anomaly analysis based on robust principal component analysis (RPCA) and a single indicator anomaly detection algorithm. The single indicator anomaly detection algorithm is composed of Isolation Forest (IF) algorithm, One-Class Support Vector Machine (SVM) algorithm, Local Outlier Factor (LOF) algorithm, and
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principle. For general and network time-consuming anomaly in the process of the MSA, we formulate different anomaly time-consuming detection strategies. We select a batch of sample data and three batches of test data of the 2020 International AIOps Challenge to debug our algorithm. According to the scoring criteria of the competition organizers, our algorithm has an average score of 0.8304 (The full score is 1) in the four batches of data. Our proposed algorithm has higher accuracy than some traditional machine learning algorithms in anomaly detection.