Distributed Honeypot Sensor Nodes for Network Threat Detection
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Solution Overview
Problem
Current cybersecurity and observability systems face challenges in efficiently filtering out non-threatening internet scanning activities from vast amounts of network data, overwhelming limited staff resources and requiring costly noise reduction efforts, while needing a system to monitor and aggregate internet traffic to identify potential threats and trends for real-time network defense updates.
Innovation Solution
A system utilizing widely distributed lightweight honeypot sensor nodes to monitor and analyze network traffic, correlate data across sensors, and produce a threat landscape through an emulation engine that simulates vulnerabilities to attract and monitor attacker interactions, thereby identifying potential cybersecurity threats and updating network defenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If log management and security tools aggregate large volumes of network data to identify threats, then threat detection capability is improved, but staff resources become overwhelmed and noise reduction becomes costly
Solution Approach 1:
The patent segments the monolithic security analysis system into distributed sensor nodes deployed across multiple locations. Each sensor node independently monitors and classifies traffic locally, dividing the overall threat detection function into smaller, manageable units that can operate autonomously while contributing to collective intelligence.
Solution Approach 2:
The patent introduces sensor nodes as intermediary components between raw network traffic and central analysis systems. These nodes act as mediators that pre-process, classify, and filter traffic data before it reaches central security operations, reducing the burden on staff and central systems while maintaining comprehensive monitoring.
2Reliability
If perimeter security systems monitor all network traffic to identify threats, then security coverage is improved, but false positives from benign scanning increase and consume resources
Solution Approach 1:
The patent applies local quality by enabling each sensor node to independently classify traffic based on local patterns and characteristics. Different nodes can develop specialized classification capabilities tailored to their specific network environments, improving overall detection accuracy while reducing false positives from uniform monitoring approaches.
Solution Approach 2:
The patent implements feedback mechanisms where sensor nodes continuously learn from classified traffic data and adjust their classification models. This feedback loop enables the system to distinguish between benign scanning and malicious activity more effectively over time, improving security coverage while reducing noise from false positives.
3Measurement precision
If distributed sensor nodes are deployed to monitor network traffic, then threat identification accuracy is improved, but system implementation complexity increases
Solution Approach 1:
The patent uses copying by deploying identical or similar sensor node templates across multiple locations. Each node is a replicated instance of a standardized design, simplifying deployment and maintenance while enabling scalable expansion. The classification models and processing logic are copied across nodes, ensuring consistent performance throughout the distributed system.
Solution Approach 2:
The patent implements universality by designing sensor nodes with multi-functional capabilities that can handle various traffic types and classification tasks. Each node serves multiple purposes including traffic monitoring, pattern recognition, anomaly detection, and data aggregation, reducing the need for specialized components and simplifying system architecture.
Data Source
AI summary
A system and methods for network action classification and analysis using widely distributed lightweight honeypot sensor nodes, comprising a plurality of network traffic sensors each configured to monitor visible network traffic, analyze monitored traffic to identify patterns, communicate with other network sensors to correlate their respective traffic data, and produce a threat landscape based on the correlated traffic data. The system and method may comprise an emulation engine configured to simulate limited services or functionalities, emulating vulnerabilities or weak points in systems. Emulation engine may comprise one or more modules configured to provide use-case specific emulation capabilities. Emulation engine may receive network traffic data from network sensors, route the network traffic to an appropriate simulated destination service associated with the network traffic, and monitor the interactions between an attacker and the simulated destination. Logged interactions may be used as an input to generate the threat landscape.