A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations

Mohammed Nasser Al-Mhiqani, Rabiah Ahmad, Z. Zainal Abidin, Warusia Yassin, Aslinda Hassan, Karrar Hameed Abdulkareem, Nabeel Salih Ali, Zahri Yunos

Research output: Contribution to journalReview articlepeer-review

37 Citations (Scopus)

Abstract

Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

Original languageEnglish
Article number5208
Number of pages41
JournalApplied Sciences (Switzerland)
Volume10
Issue number15
Early online date28 Jul 2020
DOIs
Publication statusPublished - 1 Aug 2020
Externally publishedYes

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