Maintainable Log Datasets for Evaluation of Intrusion Detection Systems

Abstract

Intrusion detection systems (IDS) monitor system logs and network traffic to recognize malicious activities in computer networks. Evaluating and comparing IDSs with respect to their detection accuracies is thereby essential for their selection in specific use-cases. Despite a great need, hardly any labeled intrusion detection datasets are publicly available. As a consequence, evaluations are often carried out on datasets from real infrastructures, where analysts cannot control system parameters or generate a reliable ground truth, or private datasets that prevent reproducibility of results. As a solution, we present a collection of maintainable log datasets collected in a testbed representing a small enterprise. Thereby, we employ extensive state machines to simulate normal user behavior and inject a multi-step attack. For scalable testbed deployment, we use concepts from model-driven engineering that enable automatic generation and labeling of an arbitrary number of datasets that comprise repetitions of attack executions with variations of parameters. In total, we provide 8 datasets containing 20 distinct types of log files, of which we label 8 files for 10 unique attack steps. We publish the labeled log datasets and code for testbed setup and simulation online as open-source to enable others to reproduce and extend our results.

Deep Learning for Anomaly Detection in Log Data: A Survey

“…II-A3) we recommend to consider alternative log data sets with different types of anomalies and to develop approaches for these cases. For example, in our earlier works [108], [109] we published log data sets where anomalies affect combinations, compositions, and distributions of event parameter values in addition to frequencies and sequences of log events.…”