Intelligent Agent Network Attack Fusion System
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Solution Overview
Problem
Current systems lack an effective method to detect and respond to cyber attacks in real-time, as they struggle with data fusion from multiple sources, threat assessment, and instantaneous response generation in complex network environments.
Innovation Solution
An Integrated Network Attack Fusion System (INAFS) is introduced, comprising an intelligent agent-based information retrieval subsystem, a rule-based inferencing mechanism, and a threat assessment and prediction mechanism, utilizing Bayesian belief networks for data fusion and situational awareness, enabling rapid detection and response to cyber threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional network monitoring systems are used to collect data from multiple sources, then data collection capability is improved, but the system cannot effectively fuse and analyze the data in real-time
Solution Approach 1:
The patent introduces an intelligent agent as an intermediary component that mediates between multiple data sources and the analysis system. The agent automatically retrieves, filters, and processes data from distributed sources, transforming raw data into meaningful information that can be analyzed in real-time. This intermediary layer resolves the contradiction by enabling effective data fusion without overwhelming the system with raw data volume.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with intelligent software agents that use inference mechanisms. Instead of relying on rigid, pre-programmed data processing rules, the system employs intelligent agents capable of autonomous decision-making and adaptive analysis, enabling real-time processing of complex, multi-source data without the computational bottlenecks of traditional systems.
2Measurement precision
If comprehensive network data is collected for thorough threat assessment, then detection accuracy is improved, but response time is delayed
Solution Approach 1:
The patent implements preliminary action by having intelligent agents continuously monitor and pre-process network data before threats materialize. The agents maintain an ongoing understanding of network baselines and anomaly patterns, so when a threat occurs, the system can immediately compare against pre-established profiles rather than starting analysis from scratch. This enables both comprehensive analysis and rapid response.
Solution Approach 2:
The patent applies partial action by focusing analysis resources on the most critical data elements and high-probability threat indicators. The intelligent agent selectively processes data based on inferred threat levels, allocating computational resources to the most significant anomalies while ignoring low-priority noise. This selective approach maintains detection accuracy while reducing overall processing time.
3Measurement precision
If manual analysis of network data is performed for accurate threat assessment, then analysis quality is improved, but automation level is reduced
Solution Approach 1:
The patent implements self-service through intelligent agents that autonomously perform data retrieval, filtering, analysis, and response generation without human intervention. The agents use built-in inference mechanisms to independently assess threats and execute countermeasures. This self-service capability achieves both high-quality analysis and full automation, resolving the contradiction between manual analysis quality and automated operation.
Solution Approach 2:
The patent transforms the nature of data analysis by changing parameters from static, rule-based processing to dynamic, intelligent inference. The system adjusts analysis depth, data selection criteria, and response strategies based on real-time conditions and threat characteristics. This parameter adaptation enables automated systems to achieve manual-quality assessment by flexibly adjusting processing parameters rather than following rigid procedures.
4Speed
If simple response mechanisms are used for rapid response generation, then response speed is improved, but response effectiveness is reduced
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple response strategies and countermeasure templates for different threat types. When a threat is detected, the intelligent agent selects and executes the appropriate pre-planned response rather than generating a response from scratch. This pre-prepared approach enables both rapid execution and effective, targeted countermeasures.
Solution Approach 2:
The patent implements dynamic response mechanisms that adapt to the specific characteristics of each detected threat. The intelligent agent adjusts response intensity, selection of countermeasures, and execution timing based on real-time threat assessment. This dynamic approach ensures responses are both rapid (automated execution) and effective (tailored to specific threat conditions) rather than using fixed, one-size-fits-all responses.
Data Source
AI summary
An improved security system for and method of detecting and responding to cyber attacks on a network or network element. The system comprises: (a) an intelligent agent-based information retrieval subsystem configured so as to automatically search for and retrieve relevant data from distributed sources; (b) a rule-based inferencing mechanism configured so as to interpret retrieved data within the situational context to support event and alert generation for cyber threat assessment and prediction; and (c) a threat assessment and prediction mechanism configured so as to capture relating to the interrelationship between cyber sensor outputs and cyber attacks.