A Novel Criterion of Reconstruction-based Anomaly Detection for Sparse-binary Data
A Novel Criterion of Reconstruction-based Anomaly Detection for Sparse-binary Data
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DOI：
10.1109/globecom42002.2020.9322452
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Heng Qiao;D. Oliveira;Dapeng Oliver Wu
中科院分区：
文献类型：
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作者：
Heng Qiao;D. Oliveira;Dapeng Oliver Wu
出于安全考虑，异常检测算法可以使用计算机使用行为信息来识别计算机系统的当前用户。然而，在这种设置中收集的数据可能是二进制的，并且非常稀疏，导致一些广泛使用的异常检测方法的性能很差。在本研究中，我们提出了一种新的重构准则，该准则受F1分数和交叉熵损失的启发，通过有效地合并正类和负类矢量元素计算的重构准则，解决了由二值和稀疏数据分布引入的类不平衡问题。实验表明，该准则可以有效地提高基于重构的异常检测方法的性能，包括主成分分析和自编码器。
Computer usage behaviour information can be used by anomaly detection algorithms to identify the current user of the computer system for security reasons. However, the data collected in this setup can be binary and very sparse, resulting in poor performance for some widely used anomaly detection methods. In this study, we propose a novel reconstruction criterion inspired by the F1 score and the cross-entropy loss, that tackles the class imbalance problem introduced by binary and sparse data distribution with effectively merging reconstruction criterion calculated from vector elements of both positive and negative classes. Our experiments show that the proposed criterion can effectively improve the performance of reconstruction based anomaly detection methods, including both the PCA and the autoencoder.