A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Information and Cyber Security · Network Security and Intrusion Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Information Security and Networking Research Group (InFORSNET), Center for Advanced Computing Technology, Faculty of Information Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
Information Technology Research and Development Centre, University of Kufa, Kufa 54001, Najaf Governorate, Iraq
CyberSecurity Malaysia, Selangor 63000, Malaysia
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