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Automatic Signature Generation for Anomaly Detection in Business Process Instance Data

机译：自动签名生成，用于业务流程实例数据中的异常检测

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摘要

Implementing and automating business processes often means to connect and integrate a diverse set of potentially flawed services and applications. This makes them an attractive target for attackers. Here anomaly detection is one of the last defense lines against unknown vulnerabilities. Whereas anomaly detection for process behavior has been researched, anomalies in process instance data have been neglected so far, even though the data is exchanged with external services and hence might be a major sources for attacks. Deriving the required anomaly detection signatures can be a complex, work intensive, and error-prone task, specifically at the presence of a multitude of process versions and instances. Hence, this paper proposes a novel automatic signature generation approach for textual business process instance data while respecting its contextual attributes. Its efficiency is shown by an comprehensive evaluation that applies the approach on thousands of realistic data entries and 240,000 anomalous data entries.
机译：实现和自动化业务流程通常意味着连接和集成各种可能存在缺陷的服务和应用程序。这使它们成为攻击者的诱人目标。在这里，异常检测是针对未知漏洞的最后防御线之一。尽管已经研究了针对流程行为的异常检测，但是到目前为止，流程实例数据中的异常现象都已被忽略，即使该数据与外部服务进行了交换，因此可能是攻击的主要来源。导出所需的异常检测签名可能是一项复杂，工作量大且容易出错的任务，特别是在存在多个流程版本和实例的情况下。因此，本文提出了一种新颖的文本业务流程实例数据自动签名生成方法，同时又考虑了其上下文属性。全面的评估表明了该方法的效率，该评估将该方法应用于数千个现实数据条目和240,000个异常数据条目。

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