Hierarchical Anomaly Detection in Self-Learning Networks
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Solution Overview
Problem
Current data visualization systems in self-learning networks face challenges in efficiently detecting and presenting anomalies, particularly in distributed environments, due to bandwidth constraints and resource limitations, making it difficult to effectively identify and mitigate Denial of Service (DoS) attacks.
Innovation Solution
A hierarchical architecture is implemented where a first device in the network maintains raw traffic flow information, detects anomalous traffic using machine learning-based anomaly detection, and provides a compressed summary to a second device for presentation, while also caching raw data for on-demand retrieval, thereby minimizing bandwidth usage and preserving the distributed nature of the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw traffic flow information is transmitted to the user interface for anomaly visualization, then the completeness of anomaly detection is improved, but network bandwidth consumption increases
Solution Approach 1:
The system segments the data transmission process into two levels: a compressed summary level for routine anomaly visualization and a raw data level for detailed analysis. The compressed summary contains essential anomaly information while filtering out redundant data, thereby reducing bandwidth consumption while maintaining detection effectiveness. Raw data is transmitted only when needed for deeper analysis.
Solution Approach 2:
The system extracts and transmits only the essential information from raw traffic flow data to the user interface. By identifying and separating critical anomaly indicators from redundant data, the system enables effective anomaly visualization without transmitting the complete raw data set, thus reducing network bandwidth requirements.
2Measurement precision
If detailed raw traffic flow information is provided for anomaly analysis, then the accuracy of anomaly detection is improved, but network resource consumption increases
Solution Approach 1:
The system applies partial action by providing only the necessary portion of raw traffic flow information related to detected anomalies, rather than transmitting all raw data. This selective data transmission maintains detection accuracy for identified anomalies while minimizing overall network resource consumption.
Solution Approach 2:
The system introduces an intermediary processing layer that receives raw traffic flow information, identifies anomalies using machine learning models, and then transmits only relevant anomaly data to the user interface. This intermediary function acts as a filter that preserves detection accuracy while reducing the quantity of data transmitted across the network.
3Reliability
If a centralized anomaly detection system is implemented, then the effectiveness of DoS attack detection is improved, but network scalability decreases
Solution Approach 1:
The system segments the anomaly detection function across multiple network devices rather than centralizing it. Each device locally processes traffic flow information and detects anomalies using machine learning models, maintaining detection effectiveness while enabling scalability. Only anomaly information is transmitted to the user interface, not complete raw data.
Solution Approach 2:
The system enables self-service anomaly detection where network devices autonomously process traffic flow information and identify anomalies using embedded machine learning models. This distributed self-service approach eliminates the need for centralized processing, thereby maintaining detection effectiveness while significantly improving network scalability.
Data Source
AI summary
In one embodiment, a first device in a network maintains raw traffic flow information for the network. The first device provides a compressed summary of the raw traffic flow information to a second device in the network. The second device is configured to transform the compressed summary for presentation to a user interface. The first device detects an anomalous traffic flow based on an analysis of the raw traffic flow information using a machine learning-based anomaly detector. The first device provides at least a portion of the raw traffic flow information related to the anomalous traffic flow to the second device for presentation to the user interface.