ProvSec: Cybersecurity System Provenance Analysis Benchmark Dataset

Madhukar Shrestha, Y. Kim, Jeehyun Oh, J. Rhee, Yung Ryn Choe, Fei Zuo, M. Park, Gang Qian
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引用次数: 1

Abstract

System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that can be used for provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. Also, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. We demonstrate the details of the dataset events and dependency analysis.
ProvSec:网络安全系统来源分析基准数据集
系统溯源取证分析已被大量的研究工作所研究。这个领域需要细粒度的数据,比如系统调用和事件字段,以跟踪事件的依赖关系。虽然之前已经提出了安全数据集的工作,但我们发现缺乏一个有用的真实攻击数据集和可用于溯源跟踪的细节。我们创建了11个易受攻击案例的新数据集,用于系统取证分析。它包括系统调用的全部细节,包括系统调用参数。使用具有真实软件漏洞和利用的真实攻击场景。此外,我们还创建了两组良性和敌对场景,这些场景被手动标记为监督机器学习分析。我们演示了数据集事件和依赖分析的细节。
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