Performance Evaluation of Container-Level Anomaly-Based Intrusion Detection Systems for Multi-Tenant Applications Using Machine Learning Algorithms

Abstract

The virtualization of computing resources provided by containers has gained increasing attention and has been widely used in cloud computing. This new demand for container technology has been growing and the use of Docker and Kubernetes is considerable. According to recent technology surveys, containers are now mainstream. However, currently, one of the major challenges rises from the fact that multiple containers, with different owners, may cohabit on the same host. In container-based multi-tenant environments, security issues are of major concern. In this paper we investigate the performance of container-level anomaly-based intrusion detection systems for multi-tenant applications. We investigate the use of Bag of System Calls (BoSC) technique and the sliding window with the classifier and we consider eight machine learning algorithms for classification purposes. We show that among the eight machine learning algorithms, the best classification results are obtained with Decision Tree and Random Forest which lead to an F-Measure of 99.8%, using a sliding window with a size of 30 and the BoSC algorithm in both cases. We also show that, although both Decision Tree and Random Forest algorithms leads to the best classification results, the Decision Tree algorithm has a shorter execution time and consumes less CPU and memory than the Random Forest.