Application of Machine learning in Detecting Insider Threat-State of Art and Survey

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dc.categoryJournal Article
dc.contributor.authorPadmavathi, G
dc.date.accessioned2017-03-28T22:40:38Z
dc.date.available2017-03-28T22:40:38Z
dc.date.issued2013
dc.departmentComputer Scienceen_US
dc.description.abstractResearch 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.en_US
dc.identifier.urihttps://ir.avinuty.ac.in/handle/avu/2248
dc.langEnglishen_US
dc.publisher.nameInternational Journal of Advanced Networking And Applicationsen_US
dc.publisher.typeInternationalen_US
dc.titleApplication of Machine learning in Detecting Insider Threat-State of Art and Surveyen_US
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