Anomaly Detection via Diffusion Map Embedding
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Solution Overview
Problem
Current network intrusion detection systems face challenges in real-time anomaly detection in high-dimensional data, struggling to identify new attacks and handle encrypted protocols, leading to high false alarm rates and missed detections due to the complexity and dynamic nature of network traffic.
Innovation Solution
The use of diffusion processes and diffusion geometries to reduce high-dimensional data into a lower-dimensional space for analysis, employing eigenfunctions of Markov matrices to construct diffusion maps and bases, which enables efficient online anomaly detection through out-of-sample extension algorithms, allowing for real-time classification of data points as normal or abnormal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional intrusion detection systems analyze high-dimensional network traffic data in real-time, then detection speed is improved, but detection accuracy deteriorates due to high false alarm rates and missed detections
Solution Approach 1:
The patent transforms high-dimensional network traffic data into a lower-dimensional embedding space using diffusion maps and out-of-sample extension algorithms. This dimensionality reduction preserves the essential structure and relationships of the data while eliminating redundant features that cause false alarms and missed detections, thereby improving detection accuracy without sacrificing real-time detection speed.
2Measurement precision
If diffusion maps and out-of-sample extension are used to reduce data dimensionality, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent pre-computes the diffusion map embedding for training data during an offline training phase, storing the embedding matrix and related parameters. During online anomaly detection, only the out-of-sample extension computation is required, which significantly reduces the computational burden compared to performing full diffusion map computation in real-time. This preliminary action separates the computationally intensive steps from the real-time detection process.
3Adaptability or versatility
If the system processes high-dimensional data with multiple features, then the ability to capture complex attack patterns is improved, but the false alarm rate increases
Solution Approach 1:
The patent extracts the essential structure and relationships from high-dimensional network traffic data by projecting it into a lower-dimensional embedding space using diffusion maps. This extraction process retains the critical features necessary for identifying attack patterns while removing redundant and noisy features that contribute to false alarms, thus achieving a balance between attack pattern recognition and false alarm reduction.
Data Source
AI summary
Detection of abnormalities in multi-dimensional data is performed by processing the multi-dimensional data to obtain a reduced dimension embedding matrix, using the reduced dimension embedding matrix to form a lower dimension (of at least 2D) embedded space, applying an out-of-sample extension procedure in the embedded space to compute coordinates of a newly arrived data point and using the computed coordinates of the newly arrived data point and Euclidean distances to determine whether the newly arrived data point is normal or abnormal.