Intelligent Clustering for Cyber Threat Pattern Detection
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Solution Overview
Problem
Current network security technologies face challenges in efficiently and timely identifying potential threats and sources of cyberattacks due to the massive, heterogeneous, and dynamic nature of network data, which often results in inadequate clustering and recognition of meaningful patterns.
Innovation Solution
An intelligent clustering system with a dual-mode architecture that includes a data modeling module, distance modeling module, and cluster tuning module, utilizing tree data models and user-defined distance functions to process and analyze data in both mass-processing and stream-processing modes, enabling real-time analysis and adaptive clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated clustering techniques are applied to process massive network security data, then the productivity of threat identification is improved, but the manufacturing precision of cluster recognition deteriorates due to data heterogeneity and dynamic characteristics
Solution Approach 1:
The patent segments the clustering process into multiple stages: data ingestion, feature extraction, cluster formation, and cluster validation. Each stage processes data with appropriate algorithms and parameters, allowing the system to maintain high productivity while improving precision through progressive refinement of cluster assignments
Solution Approach 2:
The system implements dynamic clustering where cluster parameters, algorithms, and thresholds are automatically adjusted based on data characteristics. The clustering process adapts to changing data heterogeneity and dynamics in real-time, maintaining both high processing speed and accurate cluster recognition
2Device complexity
If traditional clustering algorithms are used to analyze heterogeneous network data, then the device complexity is reduced, but the measurement precision of data pattern recognition deteriorates
Solution Approach 1:
The patent implements a universal clustering framework that can handle multiple data types (logs, packets, flows, metadata) and heterogeneous formats through a unified processing pipeline. The system automatically detects data characteristics and applies appropriate processing methods, maintaining system simplicity while achieving high pattern recognition precision across diverse network security data
3Measurement precision
If manual analysis methods are employed to identify cyber threats, then the measurement precision of threat detection is improved, but the productivity of data processing deteriorates due to the massive volume of network data
Solution Approach 1:
The patent introduces an intermediary intelligent system that bridges manual analysis precision and automated processing speed. The system uses machine learning models trained on expert analysis patterns to automatically detect threats with high accuracy, while processing massive data volumes at automated speeds. The intermediary layer translates human expertise into scalable automated detection capabilities
4Speed
If clustering algorithms process data in real-time stream mode, then the speed of threat detection is improved, but the manufacturing precision of cluster formation deteriorates due to incomplete data samples
Solution Approach 1:
The patent implements preliminary data buffering and pre-processing before stream clustering. Complete data samples are collected and pre-processed where possible, with clustering algorithms designed to handle partial information. This preliminary action ensures that stream-based clustering maintains high speed while achieving acceptable precision through robust algorithms that can infer complete patterns from incomplete samples
Data Source
AI summary
An intelligent clustering system has a dual-mode clustering engine for mass-processing and stream-processing. A tree data model is utilized to describe heterogenous data elements in an accurate and uniform way and to calculate a tree distance between each data element and a cluster representative. The clustering engine performs element clustering, through sequential or parallel stages, to cluster the data elements based at least in part on calculated tree distances and parameter values reflecting user-provided domain knowledge on a given objective. The initial clusters thus generated are fine-tuned by undergoing an iterative self-tuning process, which continues when new data is streamed from data source(s). The clustering engine incorporates stage-specific domain knowledge through stage-specific configurations. This hybrid approach combines strengths of user domain knowledge and machine learning power. Optimized clusters can be used by a prediction engine to increase prediction performance and/or by a network security specialist to identify hidden patterns.