LSTM Network for Sequential Network Traffic Threat Detection
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Solution Overview
Problem
Current network traffic classification systems face challenges in accurately identifying malicious network threats due to their reliance on manual feature design and limited contextual information, which becomes inadequate with the increasing complexity of malware samples and long-range dependencies in network communication.
Innovation Solution
The implementation of a Long Short-Term Memory (LSTM) based recurrent neural network (RNN) that automatically learns feature representations from network traffic data, including URL sequences and user connections, to classify traffic as malicious or legitimate, leveraging the sequential nature of communication data and capturing long-range dependencies without the need for manual rule-based systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature design and traditional classification systems are used, then system simplicity is maintained, but detection accuracy deteriorates due to inability to capture long-range dependencies and contextual information
Solution Approach 1:
The patent replaces manual feature engineering and traditional classification mechanisms with an automated deep learning system. Specifically, it substitutes hand-crafted features with automatic feature extraction through LSTM neural networks that learn representations directly from network traffic data, eliminating the need for manual rule design while improving detection capability
Solution Approach 2:
The patent changes the fundamental parameters of how network traffic is analyzed by transitioning from static manual feature extraction to dynamic automatic feature learning. The LSTM model adapts its internal parameters during training to capture temporal patterns and long-range dependencies in network traffic sequences, enabling accurate detection without predefined rules
2Adaptability or versatility
If traditional classification systems are used, then implementation simplicity is maintained, but ability to model sequential behavior and capture long-range dependencies deteriorates
Solution Approach 1:
The patent replaces traditional mechanical classification rules with a neural network-based sequential modeling system. The LSTM architecture automatically learns temporal patterns and sequential relationships from network traffic data, capturing long-range dependencies that are impossible to encode manually while providing adaptable detection capability
Solution Approach 2:
The system performs self-learning and self-adjustment through automatic feature extraction and representation learning. The LSTM model trains on network traffic sequences to automatically identify patterns and behaviors, eliminating the need for external manual rule provision while maintaining high adaptability to new threat types
Data Source
AI summary
A computer-implemented data processing method comprises: executing a recurrent neural network (RNN) comprising nodes each implemented as a Long Short-Term Memory (LSTM) cell and comprising links between nodes that represent outputs of LSTM cells and inputs to LSTM cells, wherein each LSTM cell implements an input layer, hidden layer and output layer of the RNN; receiving network traffic data associated with networked computers; extracting feature data representing features of the network traffic data and providing the feature data to the RNN; classifying individual Uniform Resource Locators (URLs) as malicious or legitimate using LSTM cells of the input layer, wherein inputs to the LSTM cells are individual characters of the URLs, and wherein the LSTM cells generate feature representation; based on the feature representation, generating signals to a firewall device specifying either admitting or denying the URLs.