Network Security Apparatus Load-Based Detection Allocation
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Solution Overview
Problem
Existing methods for detecting fraudulent communications in IoT networks, such as list-type detection and machine-learning-type detection, face challenges including high human costs, computational resource requirements, and inconsistent detection results, especially when multiple devices and systems coexist in a network, leading to complex installation and operation costs.
Innovation Solution
A network security system dynamically selects between list-type detection and machine-learning-type detection based on load status, allowing both methods to be used simultaneously without duplicating devices or systems, by employing a filter that allocates communication data to either or both detection types based on load conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If list-type detection is used to accurately detect fraudulent communications by inspecting communication details, then detection precision is improved, but processing time and computational resources increase enormously
Solution Approach 1:
The patent segments the detection process into two distinct modules: list-type detection for high-precision analysis of specific communication patterns, and machine-learning-type detection for rapid filtering of obvious anomalies. This segmentation allows each method to operate on appropriate subsets of traffic, balancing precision and speed.
Solution Approach 2:
The patent applies partial action by using machine-learning-type detection to handle the majority of traffic with acceptable accuracy, while reserving list-type detection for cases requiring higher precision. This avoids applying the most resource-intensive method to all traffic.
2Productivity
If machine-learning-type detection is used to detect fraudulent communications based on statistical information, then detection speed is improved, but computational resources required increase enormously
Solution Approach 1:
The patent uses machine-learning-type detection partially, applying it to traffic where rapid processing is prioritized over maximum precision. This allows high-speed detection for common patterns while conserving computational resources.
Solution Approach 2:
The detection system is segmented into two modules with different resource requirements. The machine-learning module handles bulk traffic with lower computational overhead per packet, while the list-type module handles specific cases with higher precision requirements.
3Reliability
If both list-type detection and machine-learning-type detection are used simultaneously to detect fraudulent communications, then detection effectiveness is improved, but device complexity and installation costs increase
Solution Approach 1:
The patent merges list-type detection and machine-learning-type detection into a single integrated system with a unified control unit. This combination leverages the strengths of both methods while managing complexity through centralized coordination rather than separate independent systems.
Solution Approach 2:
The control unit is designed with multi-functionality, capable of dynamically selecting and switching between list-type and machine-learning-type detection based on traffic characteristics. This universal controller manages both detection methods without requiring separate dedicated systems.
4Measurement precision
If list-type detection is used with detailed definition files to accurately identify fraudulent communications, then detection precision is improved, but human costs and operational complexity increase
Solution Approach 1:
The patent segments the detection workload so that machine-learning-type detection automatically handles pattern recognition without human intervention, while list-type detection uses pre-defined rules for specific cases. This reduces the need for manual creation and maintenance of detailed definition files.
Data Source
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AI summary
A list-type detection unit (220) performs list-type detection on communication data so as to detect a fraudulent communication. A machine-learning-type detection unit (230) performs machine-learning-type detection on communication data so as to detect a fraudulent communication. A communication acceptance unit (210) receives communication data from a network, and allocates the received communication data to at least one of the list-type detection unit and the machine-learning-type detection unit, using an allocation filter. A filter setting unit (250) determines a parameter value based on a load status of the list-type detection unit and a load status of the machine-learning-type detection unit, and sets the determined parameter value in the allocation filter.