Graph Stream Mining Pipeline for Real-Time Botnet Detection
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Solution Overview
Problem
Existing graph mining systems are inefficient and lack scalability, making them unsuitable for real-time detection of botnets in large and continuously updated network flow data sets, leading to time-consuming and error-prone processes.
Innovation Solution
A graph stream mining pipeline system that significantly parallelizes and pipelines processes to efficiently detect nodes relevant to seed nodes from large graph streaming data, using a curated malicious IP address seed generator, graph stream mining processor, and subgraph detection module to identify botnet components in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional graph mining systems are used to analyze network flow data, then detection accuracy can be maintained, but the system becomes too slow and inefficient for real-time botnet detection
Solution Approach 1:
The system segments the graph mining process into distinct pipeline stages: data ingestion, graph construction, subgraph detection, and result analysis. Each stage processes data independently and passes results to the next stage, enabling parallel processing and improving detection speed without overwhelming system complexity
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing subgraph patterns, building graph indexes, and preparing detection rules before actual botnet detection begins. This preprocessing reduces the computational burden during real-time analysis, enabling faster detection speeds
2Reliability
If the system analyzes large volumes of network flow data continuously, then detection coverage is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The system extracts and focuses only on relevant subgraphs and patterns that are indicative of botnet activity, rather than analyzing the entire graph database. By extracting only the necessary portions of data for detection, the system maintains high detection accuracy while reducing processing time
Solution Approach 2:
The system applies partial action by performing detection on sampled or representative subsets of the network flow data when full analysis is not necessary. This approach maintains adequate detection coverage while significantly reducing processing time for continuous data streams
3Productivity
If existing graph mining methods are applied to botnet detection, then detection capability is provided, but the process becomes error-prone and inefficient
Solution Approach 1:
The system implements feedback mechanisms where detection results are continuously evaluated and used to refine detection rules and parameters. False positives and negatives are analyzed to improve the precision of subgraph patterns, thereby maintaining high detection efficiency while improving accuracy over time
Data Source
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Figure 1B
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AI summary
A graph stream mining processing system and method may be used to analyze the data from a plurality of data streams. In one embodiment, the graph stream mining processing system and method may be used to detect one or more candidate botnet malicious nodes.