The ecosystem for automated offensive security tools has grown in recent years. As more tools automate offensive security techniques via Artificial Intelligence (AI) and Machine Learning (ML), it may result in vulnerabilities due to adversarial attacks. Therefore, it is imperative that research is conducted to help understand the techniques used by these security tools. Our work explores the current state of the art in offensive security tools. First, we employ an abstract model that can be used to understand what phases of an Offensive Cyber Operation (OCO) can be automated. We then adopt a generalizable taxonomy, and apply it to automation tools (such as normal automation and the use of artificial intelligence in automation). We then curated a dataset of tools and research papers and quantitatively analyzed it. Our work resulted in a public dataset that includes analysis of (n=57) papers and OCO tools that are mapped to the the MITRE ATT&CK Framework enterprise techniques, applicable phases of our OCO model, and the details of the automation technique. The results show a need for a granular expansion on the ATT&CK Exploit Public-Facing application technique. A critical finding is that most OCO tools employed Simple Rule Based automation, hinting at a lucrative research opportunity for the use of Artificial Intelligence (AI) and Machine Learning (ML) in future OCO tooling.
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How does Endpoint Detection use the MITRE ATT&CK Framework?
MITRE ATT&CK is an open-source taxonomy of adversary tactics, techniques, and procedures based on real-world observations. Increasingly, organizations leverage ATT&CK technique "coverage" as the basis for evaluating their security posture, while Endpoint Detection and Response (EDR) and Security Indicator and Event Management (SIEM) products integrate ATT&CK into their design as well as marketing. However, the extent to which ATT&CK coverage is suitable to serve as a security metric remains unclear— Does ATT&CK coverage vary meaningfully across different products? Is it possible to achieve total coverage of ATT&CK? Do endpoint products that detect the same attack behaviors even claim to cover the same ATT&CK techniques? In this work, we attempt to answer these questions by conducting a comprehensive (and, to our knowledge, the first) analysis of endpoint detection products' use of MITRE ATT&CK. We begin by evaluating 3 ATT&CK-annotated detection rulesets from major commercial providers (Carbon Black, Splunk, Elastic) and a crowdsourced ruleset (Sigma) to identify commonalities and underutilized regions of the ATT&CK matrix. We continue by performing a qualitative analysis of unimplemented ATT&CK techniques to determine their feasibility as detection rules. Finally, we perform a consistency analysis of ATT&CK labeling by examining 37 specific threat entities for which at least 2 products include specific detection rules. Combined, our findings highlight the limitations of overdepending on ATT&CK coverage when evaluating security posture; most notably, many techniques are unrealizable as detection rules, and coverage of an ATT&CK technique does not consistently imply coverage of the same real-world threats.
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- Award ID(s):
- 2055127
- PAR ID:
- 10640866
- Publisher / Repository:
- USENIX Security Symposium
- Date Published:
- ISBN:
- 978-1-939133-44-1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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