New Insider Threat Detection Method Based On Recurrent Neural Networks

Al-Mhiqani, Mohammed Nasser Ahmed and Ahmad, Rabiah and Zainal Abidin, Zaheera and S.M.M Yassin, S.M. Warusia Mohamed and Hassan, Aslinda and Mohammad, Ameera Natasha (2019) New Insider Threat Detection Method Based On Recurrent Neural Networks. Indonesian Journal of Electrical Engineering and Computer Science, 17 (3). pp. 1474-1479. ISSN 2502-4752

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Abstract

Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions.

Item Type:	Article
Uncontrolled Keywords:	Cyber security, Deep learning, Gated recurrent network, Insider, Insider threat, Neural Networks
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	21 Oct 2020 14:59
Last Modified:	21 Oct 2020 14:59
URI:	http://eprints.utem.edu.my/id/eprint/24322
Statistic Details:	View Download Statistic

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