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Two-dimensional Traceability Link Rule Mining for Detection of Insider Attacks

机译:用于检测内幕攻击的二维可追溯性链路规则挖掘

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Organizations face a growing threat of insider attacks. This paper presents a model for detecting insider malicious activities targeted at tampering the contents of files for various purposes. It employs two-dimensional traceability link rule mining to identify intrinsic file dependencies. Traceability links are traditionally used by software practitioners and researchers to uncover the relationships between programs and documents in a software system. In this research, we borrow the concept of traceability link from software engineering realm and use traceability links to model file access patterns. Activities that modify data without complying with various file traceability link rules will be identified as suspicious activities. Because file traceability links are less prone to change than individual user's file access patterns, the insider attack detection model built on traceability links is more effective than many existing systems based on usage patterns.
机译:组织面临着越来越多的内幕攻击威胁。本文介绍了检测针对各种目的篡改文件内容的内幕恶意活动的模型。它采用二维可追溯性链路规则挖掘来识别内部文件依赖项。可跟踪性链接传统上由软件从业者和研究人员使用,以揭示软件系统中的程序和文档之间的关系。在这项研究中,我们借用软件工程领域的可追溯性链路的概念,并使用可追溯性链接来模拟文件访问模式。在不符合各种文件可追溯性链接规则的情况下修改数据的活动将被确定为可疑活动。因为文件可追溯性链接不太容易变化而不是单个用户的文件访问模式,所以在可追溯性链路上构建的内幕攻击检测模型比基于使用模式的许多现有系统更有效。

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