Distributed Autonomous Agents for Network Security Analysis
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Solution Overview
Problem
As networks grow in size and complexity, centralized data analysis becomes inefficient and resource-intensive, making it difficult to effectively detect and respond to security-related issues across a large number of computing devices.
Innovation Solution
Implementing a decentralized approach where each computing device analyzes its local data and correlates results with other devices, using autonomous agents to generate reports and confirmations, thereby allowing for the detection of suspicious behaviors across the network without the need for centralized data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized data analysis is implemented, then comprehensive security monitoring is achieved, but resource consumption and processing time increase significantly
Solution Approach 1:
The patent divides the centralized data analysis system into distributed autonomous agents deployed across multiple computing devices. Each agent independently analyzes local data and correlates findings with other agents, eliminating the need to concentrate all data in one central location. This segmentation reduces resource consumption at any single point while maintaining comprehensive security monitoring through collaborative analysis.
2Reliability
If centralized data analysis is implemented, then comprehensive security monitoring is achieved, but processing speed decreases due to data transmission requirements
Solution Approach 1:
By segmenting the analysis function across distributed agents, the patent eliminates the need to transmit large volumes of data to a central location. Each agent processes local data immediately and only transmits summarized findings or correlation results, dramatically improving processing speed while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The patent implements preliminary local analysis at each computing device before correlation. Autonomous agents perform initial data processing and anomaly detection locally, preparing results in advance for correlation with other agents. This preliminary action reduces the time required for centralized processing and enables faster overall security monitoring.
3Productivity
If decentralized analysis is implemented, then resource efficiency improves, but system complexity increases
Solution Approach 1:
The patent creates universal autonomous agents that can be deployed across different computing devices regardless of the specific data type or device architecture. These multi-functional agents handle various analysis tasks and communication protocols, simplifying the overall system design despite the decentralized structure. The standardized agent interface reduces complexity by providing a consistent framework across diverse environments.
4Loss of energy
If data is distributed across computing devices, then centralized transmission burden is reduced, but coordination between devices becomes more difficult
Solution Approach 1:
The patent implements feedback mechanisms where autonomous agents exchange analysis results and correlation findings with each other and with privileged computing nodes. This feedback loop enables automatic coordination and consensus-building across distributed devices, reducing the need for complex manual coordination while maintaining efficient resource utilization. The feedback system allows devices to autonomously adjust their analysis priorities based on network-wide conditions.
Data Source
AI summary
Techniques for analyzing a dataset may be provided. For example, a configuration file may be accessed. The dataset may be analyzed based on a condition identified in the configuration file. A report may be generated and transmitted based on the analysis. Another report generated based on an analysis of another dataset according to another configuration file may be accessed. The dataset may be further analyzed based on this report to determine if a reported observation may also be associated with the dataset. If so, a confirmation may be generated and transmitted.