基于概率模型的文档历史系统及其在异常文档访问模式检测中的应用

Wenxing Peng, Taro Terao, Yoshihiro Masuda, Naofumi Yoshida, Jun Miyazaki
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引用次数: 1

摘要

我们提出了一种跟踪文档历史的设计和实现,以及它在检测异常文档访问模式中的应用。我们一直致力于文件历史系统,使我们能够追踪完整的操作访问历史,无论是电子文件还是纸质文件。文档历史系统根据用户操作对文档的依赖关系组织访问模式树。为了分析文档的访问模式树,我们提出了一种新的概率模型——多层树马尔可夫模型(MTMM)。MTMM的主要特点是对树进行树模式总结和多层次的结构抽象。我们的贡献是通过结合马尔可夫模型的层次内状态结构进行多层次的结构抽象。通过将MTMM应用于访问文档历史系统的模式树,可以检测异常模式,例如将文档发送给意外用户。通过对合成树和真实文档访问模式树的实验,验证了MTMM的可行性和有效性。
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Document History System and its Application to Abnormal Document Access Pattern Detection with a Probabilistic Model
We present a design and implementation of tracing document history and its application to detecting abnormal document access patterns. We have been working on document history system, which enables us to trace complete access history of operations for both electronic and paper documents. Document history system organizes access pattern trees based on dependency of user operations on documents. We also present a new probabilistic model, multi-layered tree Markov model (MTMM), in order to analyze access pattern trees of documents. The main feature of MTMM is to conduct tree pattern summarization and multilevel structural abstraction for trees. Our contribution is multi-level structural abstraction by incorporating hierarchical inner-state structure of Markov model. Abnormal patterns, such as sending documents to unexpected users, can be detected by applying MTMM to access pattern trees of document history system. We clarify the feasibility and effectiveness of our MTMM by several experiments for synthetic trees and real document access pattern trees.
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