Dynamic Device Clustering for Network Anomaly Detection
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Solution Overview
Problem
Current network anomaly detection systems face challenges in accurately identifying anomalies, particularly in distributed Denial of Service (DoS) attacks, due to the dynamic nature of network behaviors and the difficulty in distinguishing between normal and abnormal patterns, especially in highly dimensional spaces with limited contextual information.
Innovation Solution
The implementation of a device identity service (DIS) that provides device profile information for dynamic clustering, allowing networking devices to form clusters based on similar characteristics and receive feedback for adjustment, enabling improved anomaly detection through enhanced behavioral modeling and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device clustering is performed using traditional network traffic analysis, then anomaly detection can be implemented, but the accuracy is insufficient due to limited contextual information and difficulty in distinguishing normal from abnormal patterns
Solution Approach 1:
The system performs preliminary device profiling and clustering before anomaly detection by collecting device identity information, hardware characteristics, software configurations, and network behavior patterns from multiple sources. This preliminary organization of contextual information enables more accurate anomaly detection by establishing baseline profiles against which abnormal behaviors can be compared.
Solution Approach 2:
The patent introduces device identity services and profiling mechanisms as intermediaries between raw network traffic data and anomaly detection algorithms. These intermediaries enrich the data with contextual information about device characteristics, ownership, and normal behavior patterns, thereby improving the accuracy of anomaly detection without requiring direct analysis of raw traffic alone.
2Ease of manufacture
If static signature-based methods are used for anomaly detection, then implementation is simple, but the system cannot detect new or variant attacks such as distributed DoS attacks
Solution Approach 1:
The system transitions from static signature-based detection to dynamic behavioral analysis by continuously monitoring device behavior patterns, network traffic characteristics, and device identity information. The clustering algorithms adapt to new attack patterns by re-evaluating device profiles and updating anomaly detection models, enabling the system to detect previously unseen attack types while maintaining implementation feasibility through automated machine learning processes.
3Measurement precision
If device profiling with multiple data sources is implemented, then contextual information and clustering accuracy improve, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the device profiling system into multiple independent data collection modules, each responsible for gathering information from specific sources such as device identity services, network traffic analysis, and configuration databases. This modular segmentation allows the system to collect comprehensive contextual information while managing complexity through organized, reusable components that can be independently configured and maintained.
Data Source
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AI summary
A networking device in a network causes (1110) formation of device clusters of devices in the network exhibiting similar characteristics. The networking device receives (1115) feedback from a device identity service regarding the device clusters. The feedback is based in part on the device identity service probing the devices. The networking device adjusts (1120) the device clusters based on the feedback from the device identity service. The networking device performs (1125) anomaly detection in the network using the adjusted device clusters.