  Insiders are the trusted entities in the organization, but poses threat to
the with access to sensitive information network and resources. The insider
threat detection is a well studied problem in security analytics. Identifying
the features from data sources and using them with the right data analytics
algorithms makes various kinds of threat analysis possible. The insider threat
analysis is mainly done using the frequency based attributes extracted from the
raw data available from data sources. In this paper, we propose an image-based
feature representation of the daily resource usage pattern of users in the
organization. The features extracted from the audit files of the organization
are represented as gray scale images. Hence, these images are used to represent
the resource access patterns and thereby the behavior of users. Classification
models are applied to the representative images to detect anomalous behavior of
insiders. The images are classified to malicious and non-malicious. The
effectiveness of the proposed representation is evaluated using the CMU CERT
data V4.2, and state-of-art image classification models like Mobilenet, VGG and
ResNet. The experimental results showed improved accuracy. The comparison with
existing works show a performance improvement in terms of high recall and
precision values.
