Vector Representation Model for Network Traffic Filtering
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Solution Overview
Problem
Current network security measures, particularly for home users and small enterprises, are inadequate due to the limitations of blacklist filtering, which can only detect known malicious traffic and is slow to adapt to new threats, leaving these networks vulnerable to attacks.
Innovation Solution
A method that learns vector representations of network traffic data to classify potential maliciousness by generating a vector representation model from historical data, using a machine learning approach to identify unknown malicious flows and provide real-time classification, overcoming the limitations of traditional blacklist filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blacklist filtering is used to detect malicious traffic, then known malicious domains can be blocked, but the system cannot detect unknown malicious traffic and updates are slow
Solution Approach 1:
The system pre-computes vector representations for domain names during offline processing of historical traffic data. This preliminary action enables rapid classification of new domains without requiring real-time analysis, allowing the system to quickly adapt to unknown malicious traffic while maintaining reliable protection through pre-learned patterns
Solution Approach 2:
The patent replaces the traditional mechanical blacklist filtering mechanism with a machine learning-based vector representation system. Instead of manually maintaining and updating blacklist entries, the system uses automated vector space modeling to classify domains, enabling both reliable detection and rapid adaptation to new threats through computational learning rather than manual updates
2Reliability
If advanced traffic analysis is applied to improve security detection, then unknown threats can be identified, but the processing power required exceeds economically viable limits
Solution Approach 1:
The system segments the traffic analysis process into two distinct phases: offline pre-computation of vector representations for domain names, and online rapid classification using pre-computed vectors. This segmentation allows advanced analysis to be performed when processing power is available (offline) while maintaining low resource requirements during actual traffic filtering (online), making advanced detection economically viable
Solution Approach 2:
The system performs preliminary computation of vector representations for all domain names during an offline phase using historical traffic data. This preliminary action transfers the computationally intensive work to a time when processing power constraints are less critical, enabling accurate detection during online operation without requiring excessive real-time processing power
Data Source
AI summary
A method for learning vector representations of network traffic data offline includes: receiving historical network traffic data and a domain classification list; generating a unique domain names list based on the historical network traffic data; generating a bad domains list based on the unique domain names list and the domain classification list; modifying the unique domain names list by replacing each entry of the unique domain names list that appears in the bad domains list by a common classification label; and learning a respective vector representation for each entry of the modified unique domain names list.