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Insiders are the trusted entities in the organization, but poses threat tothe with access to sensitive information network and resources. The insiderthreat detection is a well studied problem in security analytics. Identifyingthe features from data sources and using them with the right data analyticsalgorithms makes various kinds of threat analysis possible. The insider threatanalysis is mainly done using the frequency based attributes extracted from theraw data available from data sources. In this paper, we propose an image-basedfeature representation of the daily resource usage pattern of users in theorganization. The features extracted from the audit files of the organizationare represented as gray scale images. Hence, these images are used to representthe resource access patterns and thereby the behavior of users. Classificationmodels are applied to the representative images to detect anomalous behavior ofinsiders. The images are classified to malicious and non-malicious. Theeffectiveness of the proposed representation is evaluated using the CMU CERTdata V4.2, and state-of-art image classification models like Mobilenet, VGG andResNet. The experimental results showed improved accuracy. The comparison withexisting works show a performance improvement in terms of high recall andprecision values.

Image-Based Feature Representation for Insider Threat Classification