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

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1474-1479
Number of pages6
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume17
Issue number3
DOIs
Publication statusPublished - 1 Mar 2020
Externally publishedYes

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