  While most organizations continue to invest in traditional network defences,
a formidable security challenge has been brewing within their own boundaries.
Malicious insiders with privileged access in the guise of a trusted source have
carried out many attacks causing far-reaching damage to financial stability,
national security and brand reputation for both public and private sector
organizations. Growing exposure and impact of the whistleblower community and
concerns about job security with changing organizational dynamics has further
aggravated this situation. The unpredictability of malicious attackers, as well
as the complexity of malicious actions, necessitates the careful analysis of
network, system and user parameters correlated with the insider threat problem.
Thus it creates a high dimensional, heterogeneous data analysis problem in
isolating suspicious users. This research work proposes an insider threat
detection framework, which utilizes the attributed graph clustering techniques
and outlier ranking mechanism for enterprise users. Empirical results also
confirm the effectiveness of the method by achieving the best area under the
curve value of 0.7648 for the receiver operating characteristic curve.
