AI Network Event Description Fusion for Alert Interpretation
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Solution Overview
Problem
Existing network alert systems face challenges in comprehensively interpreting multiple alerts generated from detected network events, leading to difficulties in efficient human interpretation and response.
Innovation Solution
A fusion network and security operations platform that leverages cloud-based services to collect and analyze real-time data, automatically generate alerts, and provide nearly real-time responses through a distributed intrusion detection and prevention system, integrating with existing network and security tools to unify control and visualization across decentralized networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple alerts are generated in response to detected network events, then comprehensive monitoring and detection coverage are improved, but the difficulty of comprehensively interpreting the alerts increases
Solution Approach 1:
The patent combines multiple individual alerts into a unified contextualized description that integrates alert data with network topology, asset information, and temporal-spatial relationships. This merging process transforms the overwhelming number of separate alerts into a single coherent narrative that maintains comprehensive detection coverage while dramatically improving interpretability for security analysts.
Solution Approach 2:
The system introduces an intermediary processing layer that sits between alert generation and human analysis. This intermediary component aggregates, correlates, and contextualizes multiple alerts before presenting them to security analysts, thereby reducing the cognitive load and time required for interpretation while preserving the comprehensive monitoring capabilities.
2Reliability
If multiple alerts are generated for network events, then detection comprehensiveness is improved, but the time required for human response increases
Solution Approach 1:
The system performs preliminary actions by pre-contextualizing alerts with network topology information, asset criticality data, and historical patterns before human analysts receive them. This preliminary processing reduces the time analysts need to gather basic context and understand the situation, thereby accelerating the human response time while maintaining comprehensive detection.
Solution Approach 2:
The system implements feedback mechanisms that provide security analysts with immediate contextual information about alert correlations, affected assets, and recommended actions. This feedback loop enables faster decision-making by presenting analysts with pre-processed insights rather than raw data, significantly reducing the time required for human response to detected events.
3Adaptability or versatility
If a distributed intrusion detection system is implemented, then scalability and monitoring coverage are improved, but system complexity increases
Solution Approach 1:
The patent segments the intrusion detection system into distributed components that operate autonomously at different network nodes while communicating through standardized interfaces. This segmentation enables the system to scale with network size by adding modular detection units without proportionally increasing overall system complexity, as each segment follows the same design patterns and communication protocols.
Solution Approach 2:
The system implements universal detection components that can function across multiple network types and environments through standardized abstraction layers. This universality allows the same core detection logic to serve diverse network segments, reducing the need for custom complex implementations at each node and simplifying the overall distributed architecture while maintaining high scalability.
Data Source
AI summary
Various techniques for generating enhanced descriptions of detected network events for efficient human interpretation and response are disclosed. In some embodiments, event data associated with a detected network event is analyzed using an artificial intelligence based framework, and an output is generated using the artificial intelligence based framework that comprises a description of the detected network event.