Network Flow Embedding via Deep Neural Networks
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Solution Overview
Problem
Current technologies lack effective methods for learning meaningful vector embeddings applicable to network traffic analysis, which is crucial for detecting anomalies and improving cybersecurity, as existing techniques like word2vec are not directly applicable to network traffic data.
Innovation Solution
A deep neural network architecture is employed to project long temporal sequences of network flow data into compressed representations, preserving relevant information for anomaly detection and clustering, using recurrent neural networks (RNNs) to learn embeddings that capture protocol-related and temporal properties of network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If long temporal sequences of flow data are stored and processed in full, then complete information is preserved for analysis, but memory usage and processing power requirements increase significantly
Solution Approach 1:
The patent extracts only the essential temporal patterns and statistical features from long flow data sequences, projecting them into compressed vector representations that retain the most relevant information for anomaly detection while discarding redundant data, thereby reducing memory requirements while preserving analytical value
Solution Approach 2:
The patent transforms the data representation parameters by converting raw flow sequences into fixed-dimensional vector embeddings through neural network processing, changing the state from variable-length detailed records to fixed-length compressed representations that preserve essential characteristics while reducing storage needs
2Measurement precision
If long temporal sequences of flow data are processed in full, then complete analysis is achieved, but computation time increases
Solution Approach 1:
The patent performs preliminary processing by pre-training neural network models to learn optimal vector representations of flow sequences offline, so that during actual anomaly detection, only lightweight vector comparisons are needed, significantly reducing real-time computation time while maintaining detection accuracy
Solution Approach 2:
The patent creates compressed vector copies of the essential characteristics of flow sequences that can be processed and compared much faster than the original detailed sequences, enabling rapid anomaly detection without sacrificing the ability to identify malicious patterns
3Reliability
If detailed flow data is stored and processed, then comprehensive anomaly detection is possible, but system complexity increases
Solution Approach 1:
The patent replaces complex rule-based anomaly detection systems with neural network-based vector representation learning, where the model automatically learns relevant features and patterns from data, reducing the need for manual feature engineering and complex detection logic while improving reliability
Data Source
AI summary
Disclosed is an improved method, system, and computer program product for learning representations or embeddings of network flow traffic. The disclosed invention operates on network flow data which are then used as inputs to a deep-learning architecture that learns to embed the data into a vector space.