arXiv subjects

Brian Jewell

Publications and source records attributed to Brian Jewell.

3 recordsLinked to original sources

arXiv2023

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.

cs.CR↗

arXiv2023

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.

cs.CR↗

arXiv2022

Beyond the Hype: A Real-World Evaluation of the Impact and Cost of Machine Learning-Based Malware Detection

In this paper, we present a scientific evaluation of four prominent malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously- and never-before-seen files? Is it worth purchasing a network-level malware detector? To identify weaknesses, we tested each tool against 3,536 total files (2,554 or 72\% malicious, 982 or 28\% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone \& Bridges. While the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool may still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files -- 37% of malware tested, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given.

cs.CR↗