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Image-based feature representation for insider threat classification

Gayathri, R. G., Sajjanhar, Atul and Xiang, Yong 2020, Image-based feature representation for insider threat classification, Applied sciences, vol. 10, no. 14, pp. 1-17, doi: 10.3390/app10144945.

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Title Image-based feature representation for insider threat classification
Author(s) Gayathri, R. G.
Sajjanhar, AtulORCID iD for Sajjanhar, Atul orcid.org/0000-0002-0445-0573
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Journal name Applied sciences
Volume number 10
Issue number 14
Start page 1
End page 17
Total pages 17
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020
ISSN 2076-3417
Keyword(s) cybersecurity
deep learning
insider threat
transfer learning
machine learning
image classification
Summary Cybersecurity attacks can arise from internal and external sources. The attacks perpetrated by internal sources are also referred to as insider threats. These are a cause of serious concern to organizations because of the significant damage that can be inflicted by malicious insiders. In this paper, we propose an approach for insider threat classification which is motivated by the effectiveness of pre-trained deep convolutional neural networks (DCNNs) for image classification. In the proposed approach, we extract features from usage patterns of insiders and represent these features as images. Hence, images are used to represent the resource access patterns of the employees within an organization. After construction of images, we use pre-trained DCNNs for anomaly detection, with the aim to identify malicious insiders. Random under sampling is used for reducing the class imbalance issue. The proposed approach is evaluated using the MobileNetV2, VGG19, and ResNet50 pre-trained models, and a benchmark dataset. Experimental results show that the proposed method is effective and outperforms other state-of-the-art methods.
Language eng
DOI 10.3390/app10144945
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30140480

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.