Mitigating the Insider Threat with High-Dimensional Anomaly Detection

In recent years, steganalyzers are intelligently detecting the stego images with high detection rate using high dimensional cover representation. And so the steganographers are working towards this issue to protect the cover element dependency and to protect the detection of hiding secret messages. Any steganalysis algorithm may achieve its success in two ways: 1) extracting the most sensitive features to expose the footprints of message hiding; 2) designing or building an effective classifier engine to favorably detect the stego images through learning all the stego sensitive features. In this chapter, the authors improve the stego anomaly detection using the second approach. This chapter presents a comparative review of application of the machine learning tools for steganalysis problem and recommends the best classifier that produces a superior detection rate.

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Variable Selection for Correlated High-Dimensional Data with Infrequent Categorical Variables: Based on Sparse Sample Regression and Anomaly Detection Technology

Intelligent Decision Technologies - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-16-2765-1_9 ◽

2021 ◽

pp. 109-125

Author(s):

Yuhei Kotsuka ◽

Sumika Arima

Keyword(s):

Variable Selection ◽

Anomaly Detection ◽

High Dimensional Data ◽

Categorical Variables ◽

High Dimensional ◽

Detection Technology ◽

Selection For

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An Anomaly Detection Method Based on GCN and Correlation of High Dimensional Sensor Data in Power Grid System

10.1007/978-3-030-90196-7_38 ◽

2021 ◽

pp. 444-454

Author(s):

Liu Weiwei ◽

Lei Shuya ◽

Zheng Xiaokun ◽

Li Han ◽

Wang Xinyu ◽

...

Keyword(s):

Anomaly Detection ◽

Detection Method ◽

Power Grid ◽

Sensor Data ◽

High Dimensional ◽

Grid System

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Graph Laplacian for image anomaly detection

Machine Vision and Applications ◽

10.1007/s00138-020-01059-4 ◽

2020 ◽

Vol 31 (1-2) ◽

Cited By ~ 1

Author(s):

Francesco Verdoja ◽

Marco Grangetto

Keyword(s):

Anomaly Detection ◽

State Of The Art ◽

Computational Cost ◽

Gaussian Model ◽

Graph Laplacian ◽

High Dimensional ◽

Spatial Awareness ◽

Detection Problem ◽

Multivariate Gaussian Model ◽

And Inversion

Abstract Reed–Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD’s limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.

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