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- W3151802645 abstract "在异例察觉，挑战是怎么为一个用户的动态行为建模。许多以前的工作基于固定长度模型代表用户行为。克服他们的缺点，我们与可变长度的序列的状态基于分离时间的 Markov 链(DTMC ) 建议一个新奇方法。方法第一产生不同长度的多重壳命令溪流并且把他们合为一般序列的图书馆。然后，状态根据一个有效用户的可变长度的行为的模式被定义，它改进用户介绍的精确和适应性。随后，转变概率矩阵被创造。以便减少计算复杂性，分类价值仅仅由转变可能性被决定，然后与滑动窗户变光滑，并且最后过去常在正常和反常行为之间区别。来自 Purdue 大学和 AT&T 香农实验室的数据集上的二实验评估证明建议方法能完成更高的察觉精确性并且比另外的传统的方法要求更少的存储器。" @default.
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- W3151802645 date "2011-01-01" @default.
- W3151802645 modified "2023-09-26" @default.
- W3151802645 title "Anomaly detection of user behavior based on DTMC with states of variable-length sequences" @default.
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