Network Security System Using Connectivity Matrix for Activity Classification
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Solution Overview
Problem
Traditional network security systems rely on blacklists and whitelists, which are ineffective in detecting zero-day threats and can be over-inclusive, failing to accurately identify malicious network activities in real-time.
Innovation Solution
A network security system that extracts a connectivity matrix from HTTP network logs to train a deep learning architecture, allowing it to generate a general data representation and detect discordant network activities, distinguishing between human-initiated and machine-initiated activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional blacklists and whitelists are used for network security, then implementation is simple, but detection accuracy for malicious activities is poor
Solution Approach 1:
The patent replaces traditional mechanical rule-based security systems (blacklists/whitelists) with a deep learning-based automated system. The system uses neural networks to automatically analyze network logs, extract features, and detect malicious activities without manual rule configuration, thereby improving detection accuracy while managing complexity through automation.
Solution Approach 2:
The deep learning system performs self-training by automatically extracting features from network logs and generating detection models without requiring manual intervention. The system continuously learns from new data, adapting to emerging threats autonomously, which improves detection precision while reducing the need for complex manual maintenance.
2Adaptability or versatility
If traditional security methods are used, then system complexity is low, but responsiveness to zero-day threats is ineffective
Solution Approach 1:
The patent implements a dynamic security system where the deep learning model continuously adapts to new threats. The system dynamically extracts features from evolving network logs and updates its detection capabilities in real-time, enabling it to respond to zero-day threats that static rule-based systems cannot detect.
Solution Approach 2:
The system performs preliminary analysis by continuously monitoring and learning from normal network behavior patterns before malicious activities occur. By establishing baseline behavior models in advance, the system can quickly identify and respond to anomalous zero-day threats as they emerge, rather than waiting for signature-based detection.
3Measurement precision
If deep learning architecture is implemented, then detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent segments the network log analysis process into distinct stages: log parsing, feature extraction, model training, and detection. This segmentation allows each component to be optimized independently, reducing overall processing time while maintaining high detection accuracy through specialized processing at each stage.
Solution Approach 2:
The system implements partial processing by extracting only the most relevant features from network logs rather than analyzing all possible data points. This selective approach reduces computational overhead and processing time while maintaining sufficient detection accuracy for identifying malicious activities.
Data Source
AI summary
Aspects are generally directed to network security systems and methods of monitoring network activity. In one example, a network security system includes and interface to receive a Hypertext Transfer Protocol (HTTP) network log that includes a matrix of data, a feature extraction component configured to extract a connectivity matrix from the HTTP network log based on a recurring pattern within the matrix of data, and a training module configured to provide deep learning architecture training data based on the connectivity matrix. The system may include a deep learning architecture configured to receive and propagate the training data through one or more layers thereof to train the one or more layers, and being configured to generate a general data representation of the HTTP network log. The system may include a behavior analytics component to detect a discordant network activity within the HTTP network log based on the general data representation.