Distributed Sensor Node Correlation for Network Threat Filtering
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Solution Overview
Problem
Existing cybersecurity systems face challenges in efficiently filtering and analyzing internet traffic to identify potential threats and aberrations due to the noise generated by large volumes of data, requiring a system that can aggregate, analyze, and forecast scanning and utilization patterns to support network security policies.
Innovation Solution
A system utilizing widely distributed sensor nodes and cloud-based processing to monitor and aggregate internet traffic, analyze patterns, and identify potential cybersecurity threats, incorporating threat landscapes into network security policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large volumes of cyber data are aggregated and analyzed, then comprehensive threat identification capability is improved, but noise and false alerts increase making analysis tedious and costly
Solution Approach 1:
The patent segments the cyber threat detection system into multiple specialized components: distributed sensor nodes for data collection, cloud-based processing systems for analysis, and sector-specific analysis modules. This segmentation allows each component to handle specific aspects of threat detection, improving overall reliability while managing noise through specialized processing pipelines for different data types and threat categories.
Solution Approach 2:
The patent introduces intermediary elements including sector-specific threat intelligence feeds, geographic risk databases, and attribution databases that act as mediators between raw cyber data and threat analysis. These intermediaries pre-process and contextualize data before it reaches analysts, filtering out benign signals and highlighting genuine threats, thereby improving signal-to-noise ratio.
2Area of stationary object
If distributed sensor nodes are deployed widely, then traffic monitoring coverage is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent merges the operations of multiple distributed sensor nodes into a unified cloud-based processing system. Individual sensor nodes collect and pre-process local traffic data, then aggregate findings to the central cloud platform which performs correlated analysis across all nodes. This merging approach maintains wide monitoring coverage while reducing coordination complexity through standardized data formats and centralized analysis logic.
Solution Approach 2:
The patent designs sensor nodes with universal, multi-functional capabilities that can detect various types of cyber threats (scanning, attacks, anomalies) using the same hardware and software platform. This universality simplifies system coordination as all nodes operate with identical protocols and data structures, reducing the complexity of managing heterogeneous distributed systems while maintaining comprehensive coverage.
3Measurement precision
If comprehensive traffic analysis is performed to identify all potential threats, then detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and contextualizing cyber traffic data before full analysis. Sector-specific threat intelligence feeds and geographic risk databases pre-mark suspicious patterns and high-risk sources, allowing the system to prioritize analysis of likely threats. This preliminary tagging and contextualization maintains high detection accuracy while reducing the time needed for comprehensive analysis of all traffic.
Solution Approach 2:
The patent applies partial analysis actions by focusing computational resources on specific high-value targets identified through preliminary filtering. Rather than performing exhaustive analysis on all traffic equally, the system concentrates detailed analysis on packets from marked suspicious sources, sector-specific threat patterns, and geographically risky regions. This selective partial analysis maintains high accuracy for critical threats while reducing overall processing time and resource consumption.
Data Source
AI summary
A system for network traffic classification using distributed sensor nodes is provided, comprising a plurality of network traffic sensors each configured to monitor visible network traffic, analyze the monitored traffic to identify patterns, communicate with other network sensors to correlate their respective traffic data, produce a threat landscape based on the correlated traffic data, identify a potential cybersecurity threat based on the threat landscape, and export the analyzed traffic and threat landscape for use by external systems.