Testing SOAR Tools in Use

Modern security operation centers (SOCs) rely on operators and a tapestry of logging and alerting tools with large scale collection and query abilities. SOC investigations are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, and correlate the data into information and then document results in a ticketing system. Security orchestration, automation, and response (SOAR) tools are a new technology that promise to collect, filter, and display needed data; automate common tasks that require SOC analysts' time; facilitate SOC collaboration; and, improve both efficiency and consistency of SOCs. SOAR tools have never been tested in practice to evaluate their effect and understand them in use. In this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and 6 commercial SOAR tools. Our contributions include the experimental design, itemizing six characteristics of SOAR tools and a methodology for testing them. We describe configuration of the test environment in a cyber range, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. We found that SOAR configuration is critical, as it involves creative design for data display and automation. We found that SOAR tools increased efficiency and reduced context switching during investigations, although ticket accuracy and completeness (indicating investigation quality) decreased with SOAR use. Our findings indicated that user preferences are slightly negatively correlated with their performance with the tool; overautomation was a concern of senior analysts, and SOAR tools that balanced automation with assisting a user to make decisions were preferred

AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors

This work presents an evaluation of six prominent commercial endpoint malware detectors, a network malware detector, and a file-conviction algorithm from a cyber technology vendor. The evaluation was administered as the first of the Artificial Intelligence Applications to Autonomous Cybersecurity (AI ATAC) prize challenges, funded by / completed in service of the US Navy. The experiment employed 100K files (50/50% benign/malicious) with a stratified distribution of file types, including ~1K zero-day program executables (increasing experiment size two orders of magnitude over previous work). We present an evaluation process of delivering a file to a fresh virtual machine donning the detection technology, waiting 90s to allow static detection, then executing the file and waiting another period for dynamic detection; this allows greater fidelity in the observational data than previous experiments, in particular, resource and time-to-detection statistics. To execute all 800K trials (100K files $\times$ 8 tools), a software framework is designed to choreographed the experiment into a completely automated, time-synced, and reproducible workflow with substantial parallelization. A cost-benefit model was configured to integrate the tools' recall, precision, time to detection, and resource requirements into a single comparable quantity by simulating costs of use. This provides a ranking methodology for cyber competitions and a lens through which to reason about the varied statistical viewpoints of the results. These statistical and cost-model results provide insights on state of commercial malware detection