Distributed Enforcement Points for Adaptive Threat Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current security threat detection and corrective action approaches in systems are centralized and manual, leading to time-consuming processes, errors in threat detection, and unsuitability for distributed systems.
Innovation Solution
A distributed system and method for detecting security threats and performing corrective actions using data collected from multiple devices, where each device performs distinct actions based on localized data analysis, with a controller determining instructions for each device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If centralized manual approach is used for threat detection and corrective action, then system control and decision-making are simplified, but time consumption increases and error rates rise
Solution Approach 1:
The patent segments the centralized threat detection system into distributed enforcement points across multiple devices. Each enforcement point independently analyzes local data and executes corrective actions, eliminating the time-consuming centralized manual review process while maintaining systematic control through shared machine learning models.
Solution Approach 2:
The enforcement points are designed to autonomously detect threats and execute corrective actions without requiring manual human intervention. The system uses automated machine learning models that continuously learn from data and automatically determine appropriate corrective actions, significantly reducing time consumption compared to manual approaches.
2Reliability
If centralized manual approach is used for threat detection, then system complexity is reduced, but detection accuracy and reliability deteriorate
Solution Approach 1:
The system divides threat detection into multiple independent enforcement points distributed across different devices. Each enforcement point maintains local analysis capabilities and can independently detect threats, improving overall detection reliability through distributed redundancy while managing complexity through modular architecture.
Solution Approach 2:
The patent combines centralized machine learning model training with distributed enforcement point execution. The system merges the advantages of centralized intelligence (for accurate threat detection models) with distributed execution (for improved reliability and reduced single-point failure risk).
3Productivity
If centralized approach is used for corrective action determination, then coordination between devices is simplified, but response speed and productivity decrease
Solution Approach 1:
The enforcement points are pre-configured with machine learning models and decision-making capabilities before deployment. This preliminary preparation enables them to immediately execute corrective actions upon detecting threats without requiring real-time centralized coordination, significantly improving response speed while maintaining coordinated behavior through pre-established protocols.
4Loss of time
If manual analysis of collected data is used, then error rates in threat detection are reduced through human oversight, but time consumption and loss of time increase significantly
Solution Approach 1:
The patent replaces manual human analysis with automated machine learning models that process threat data. These models use advanced algorithms to analyze collected data with high precision, eliminating the time-consuming nature of manual analysis while maintaining or improving detection accuracy through continuous learning and adaptation.
Data Source
AI summary
Described embodiments provide systems and methods for performing actions based on data of devices. A controller executing on at least one server may receive a first dataset from a first agent of a first device intermediary between a first plurality of client devices and a first plurality of servers. The first dataset may comprise a subset of data tracked at the first device and available to the first agent. The controller may receive a second dataset from a second agent of a second device intermediary between a second plurality of client devices and a second plurality of servers. The second dataset may comprise a subset of data tracked at the second device and available to the second agent. According to the first dataset and the second dataset, the controller may send an instruction to at least one of the first device, the second device or a third device.