Application of Machine learning in Detecting Insider Threat-State of Art and Survey
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2013
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Abstract
Research in computer security is more focused to prevent unauthorized and illegitimate access to systems and
information. But, many times, the most damaging malicious activity is the result of internal misuse within an
organization, which has not drawn much attention. Data Exfiltration refers to illegitimate transfer of data out of a
given organization or network. Organizations employ security solutions like IDS, IPS and firewalls at the perimeter
level to safe guard their network from external attacks. Insider attacks in the recent decade poses serious impact on
the organization in terms of confidentiality and reputation. Machine learning algorithms and techniques has provided
solutions to many of the complex real time problems in diversified fields and of great help in decision making and
taking preventive and corrective measures. Many of soft computing techniques have been applied in intrusion
detection in the recent years to detect and to prevent network intrusions by both external and internal attackers. This
paper presents overview of insider threat, its current state of art in research, research challenges, data exfiltration
steps, detailed on the machine learning approaches applied to address this problem.