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Design and Analysis for Fall Detection System Simplification

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Multi-homed abnormal behavior detection algorithm based on fuzzy particle swarm cluster in user and entity behavior

Jingyang Cui^1,2, Guanghua Zhang³, Zhenguo Chen⁴

¹School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China.

Scientific Reports

|December 26, 2022

Summary

This study introduces a novel User and Entity Behavior Analytics (UEBA) model using fuzzy particle swarm clustering for enhanced anomaly detection. The new method significantly improves the identification of abnormal behavior, bolstering defenses against unknown cyber threats.

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Area of Science:

Cybersecurity
Machine Learning
Anomaly Detection

Background:

Existing User and Entity Behavior Analytics (UEBA) methods often use deterministic algorithms, limiting them to single-category labeling and intra-category comparisons.
This limitation hinders the efficient identification of complex threat events within enterprise networks.
A need exists for advanced UEBA techniques capable of detecting multi-faceted abnormal behaviors.

Purpose of the Study:

To propose an improved UEBA model for detecting multi-homed abnormal behavior.
To enhance the efficiency and accuracy of anomaly detection in cybersecurity.
To address the limitations of single-category analysis in current UEBA approaches.

Main Methods:

Developed a novel model for detecting multi-homed abnormal behavior utilizing fuzzy particle swarm clustering.
Optimized entity and user behavior similarity measurement using behavior frequency-inverse entities frequency (BF-IEF) technology.
Integrated particle swarm optimization into fuzzy clustering to accelerate centroid searching and avoid local optima.
Calculated the nearest neighbor relative anomaly factor (NNRAF) across multiple fuzzy categories and employed boxplots for outlier detection.

Main Results:

The proposed model significantly improves the detection of abnormal behavior compared to traditional UEBA methods.
Achieved high performance metrics: 0.92 accuracy, 0.96 precision, 0.90 recall, and 0.93 F1-score.
Demonstrated enhanced ability to identify threats and resist unknown cyber threats in practical applications.

Conclusions:

The fuzzy particle swarm clustering-based UEBA model effectively overcomes the limitations of single-class evaluation.
The method offers superior abnormal behavior detection capabilities, crucial for modern information systems.
This approach enhances an organization's resilience against sophisticated and evolving cyber threats.