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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Ameera Natasha Mohammad
Universiti Teknikal Malaysia Melaka

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New insider threat detection method based on recurrent neural networks Mohammed Nasser Al-mhiqani; Rabiah Ahmad; Zaheera Zainal Abidin; Warusia Yassin; Aslinda Hassan; Ameera Natasha Mohammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 3: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i3.pp1474-1479

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.
Co-Authors Aslinda Hassan Mohammed Nasser Al-mhiqani Rabiah Ahmad Warusia Yassin Zaheera Zainal Abidin