Netflow Learning Models for Scalable Botnet Traffic Detection
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Solution Overview
Problem
Existing network security systems face challenges in identifying botnet traffic due to the scalability issues with device-based analysis methods like deep packet inspection, which are resource-intensive and difficult to implement in modern networks with billions of connections.
Innovation Solution
Implementing connection-based analysis using learning models, such as machine and deep learning models, to analyze netflow data for identifying botnet traffic, reducing the need for device-based analysis and enabling scalability and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep packet inspection is used to identify botnet traffic, then measurement precision is improved, but use of energy and device complexity increase significantly
Solution Approach 1:
The patent extracts only the essential flow characteristics (duration, packet count, byte count) from complete packet data, avoiding the need for deep packet inspection. This extraction approach maintains identification accuracy while dramatically reducing processing power consumption by analyzing only metadata rather than full packet contents.
Solution Approach 2:
The patent creates simplified copies of traffic data in the form of flow records containing key characteristics (duration, packets, bytes) rather than analyzing original packet streams. This copying method preserves the essential information needed for botnet detection while reducing the computational burden to a manageable level.
2Measurement precision
If deep packet inspection is used on all data transmissions, then botnet traffic identification accuracy is improved, but productivity decreases due to scaling difficulties
Solution Approach 1:
The system extracts only critical flow characteristics (duration, packet count, byte count) from traffic streams, enabling rapid analysis that scales to modern network volumes. This extraction methodology maintains detection accuracy while processing millions of connections without bottlenecking network throughput.
Solution Approach 2:
The patent changes the analysis parameters from detailed packet inspection to aggregated flow metrics. By monitoring duration, packet counts, and byte counts at flow level rather than packet level, the system achieves scalable processing that maintains productivity while preserving botnet detection capability.
3Measurement precision
If device-based analysis is used to identify botnet devices, then identification accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts identification capability from device-level analysis and relocates it to flow-level characteristics. By measuring duration, packet counts, and byte counts of connections, the system identifies botnet devices without complex device profiling, reducing analysis system complexity while maintaining identification accuracy.
Solution Approach 2:
The patent introduces flow characteristics as an intermediary between raw packet data and device identification. Instead of directly analyzing device properties or packet contents, the system uses flow metrics (duration, packets, bytes) as intermediate measurements that simplify the identification process and reduce system complexity.
Data Source
AI summary
Creating and using learning models to identify botnet traffic can include obtaining netflow data associated with a connecting device that is communicating with a carrier network. The netflow data can represent communications associated with the connecting device. Data features associated with the communications can be extracted. The data features can include statistical information associated with the communications. A learning model based on the data features extracted from the netflow data can be trained. A prediction using the learning model can be generated, and an action based on the prediction can be taken.