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Image-based feature representation for insider threat classification

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journal contribution
posted on 2020-01-01, 00:00 authored by R G Gayathri, Atul SajjanharAtul Sajjanhar, Yong XiangYong Xiang
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.

History

Journal

Applied sciences

Volume

10

Issue

14

Pagination

1 - 17

Publisher

MDPI

Location

Basel, Switzerland

eISSN

2076-3417

Language

eng

Publication classification

C1 Refereed article in a scholarly journal