Insider threats pose a critical challenge for securing computer networks and systems. They
are malicious activities by authorised users that can cause extensive damage, such as …

The objective and universal nature of user behavior data make it the primary data for insider
threat detection. Existing solutions treat user behavior as atomic symbols and do not …

Insider threat detection has been a challenging task over decades; existing approaches
generally employ the traditional generative unsupervised learning methods to produce …

Class imbalance and overlap coupling is the primary cause of performance degradation for
classifiers. Unfortunately, it always occurs. In this paper, we propose a hybrid sampling …

User behavior threat detection is important for the protection of network system security.
Traditional supervised modeling methods and unbalanced sample data lead to a high false …

Insider threat analysis techniques based on machine learning provide convenient and
effective automated detection of internally generated cyberattacks. When data are …

Insider threat problems have occurred frequently and caused significant damage to
organizations. Many existing techniques represent the user activities recorded in audit data …

Insider threats are a massive threat to healthcare organizations, causing the vast majority of
data breaches and resulting in severe negative consequences for a healthcare organization …

Anomaly detection aims to build a decision model that estimates the class of new data
based on historical sample features. However, the distance between samples in the feature …

With the exponential increase in technology use, insider threats are also growing in scale
and importance, becoming one of the biggest challenges for government and corporate …