Dark Matter Scanning via Segmented Agents
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Solution Overview
Problem
Conventional network scanning methods are ineffective in identifying and managing dark matter computing systems and devices within a network, posing security and authenticity risks and impacting network performance.
Innovation Solution
A method and system involving a master server with a dark matter server application that uses scanning agents to establish secure communication links, perform scans, and parse results to differentiate between known and unknown hosts, with a scheduling module and commander module to manage scanning jobs and determine dark matter presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive or indirect methods are used to monitor network traffic for endpoint information, then the scanning process is less intrusive, but the ability to accurately identify and determine network endpoints is insufficient
Solution Approach 1:
The system divides the scanning function into multiple components: a master server that manages scanning jobs, multiple scanning agents deployed across the network that execute scans, and a database system that stores and processes endpoint information. This segmentation allows each component to specialize in specific tasks, improving overall identification accuracy while distributing system complexity across multiple manageable elements rather than concentrating it in a single complex device
Solution Approach 2:
The patent introduces scanning agents as intermediary components that act between the master server and network endpoints. These agents are deployed on various network devices and serve as local scanning points, collecting endpoint information and reporting back to the master server. This intermediary approach enables more accurate local endpoint detection while the master server coordinates the overall scanning operation, effectively managing complexity through hierarchical organization
2Reliability
If conventional scanning tools are used to determine endpoint types and capabilities, then basic network inventory is obtained, but dark matter computing systems and devices cannot be identified
Solution Approach 1:
The system performs preliminary actions by deploying scanning agents across the network before conducting the actual dark matter detection. These agents are pre-positioned on various network devices and continuously monitor network traffic and endpoint characteristics. This preliminary presence allows the system to detect unauthorized or unknown devices (dark matter) as they appear or communicate, rather than relying solely on periodic active scans, thereby improving both security reliability and detection productivity
Solution Approach 2:
The patent implements feedback mechanisms where scanning agents continuously report endpoint information and detection results back to the master server. The master server analyzes this feedback data, compares it against known endpoint profiles, and identifies anomalies that may indicate dark matter systems. This continuous feedback loop enables the system to maintain high reliability for security monitoring while improving productivity in detecting unauthorized devices through real-time analysis of network behavior patterns
3Measurement precision
If multiple scanning agents are deployed across the network to perform scans, then coverage and detection capability are improved, but communication management and coordination become more complex
Solution Approach 1:
The patent merges the management of multiple scanning agents into a single master server that centralizes control over scanning job creation, distribution, and result collection. Instead of managing each agent independently, the master server consolidates agent coordination functions, allowing operators to manage the entire scanning operation through a single interface. This merging approach maintains high detection accuracy through multiple agents while significantly simplifying operation and reducing management complexity
Solution Approach 2:
The scanning agents are designed with multi-functionality, capable of performing various scanning tasks including endpoint discovery, capability assessment, and dark matter detection. The master server also serves multiple functions: managing agents, coordinating scans, analyzing results, and maintaining the endpoint database. This universal design allows the system to achieve comprehensive dark matter detection accuracy through versatile agents while simplifying operation through the master server's ability to handle multiple management tasks through a unified interface
Data Source
AI summary
A method and system for scanning a computing system network for dark matter computing systems and computing devices. The method includes establishing a communication link between a master server and at least one target scanning agent that has at least one network computing system coupled thereto, creating a scanning job for the target scanning agent, building a scanning job command based on the scanning job, sending the scanning job command to the target scanning agent, receiving scanning job results from the target agent, parsing through the received scanning job results for identifying information of hosts in the network computing system detected during the scanning job, determining which detected hosts are known hosts and which detected hosts are unknown hosts based on the identifying information, and comparing the identifying information of the unknown hosts to reference identifying information to determine which of the unknown hosts are dark matter.