Techniques and countermeasures for preventing insider threats

Area of Science:

• Computer Science
• Cybersecurity
• Information Security

Background:

• The increasing reliance on technology has led to significant security challenges.
• Insider threats pose a greater risk than external attacks due to authorized access.
• Organizations invest heavily in protecting data confidentiality, integrity, and availability.

Purpose of the Study:

• To review and classify existing techniques for preventing insider attacks.
• To propose a unified classification model for insider threat prevention approaches.
• To identify key factors, challenges, and future research directions in this field.

Main Methods:

• A comprehensive literature review using the grounded theory method.
• Development of a unified classification model for prevention approaches.
• Analysis and comparison of theoretical and empirical factors affecting effectiveness.

Main Results:

• Insider threat prevention approaches are classified into biometric-based (physiological, behavioral, physical) and asset-based (host, network, combined) categories.
• Key factors influencing effectiveness include datasets, feature domains, algorithms, and evaluation metrics.
• Empirical validation of reviewed approaches using grounded theory.

Conclusions:

• A systematic classification of insider threat prevention methods is presented.
• Understanding influencing factors and challenges is crucial for deploying effective systems.
• Identified research gaps and recommendations for future work in insider threat prevention.