Adaptive Network Filter for Dynamic Attack Severity Estimation
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Solution Overview
Problem
Existing network attack detection systems face challenges in accurately classifying network traffic, often resulting in false positives and false negatives, and require operator intervention for sensitivity adjustments, which is time-consuming and inefficient under dynamic traffic conditions.
Innovation Solution
An apparatus with an estimation means and an optimization means that automatically adjusts the sensitivity of a filter by determining accurate attack severity and updating parameters to minimize costs associated with false negatives and false positives, allowing for real-time optimization of network traffic filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the sensitivity of the attack detection filter is increased to reduce false negatives (accepting bad traffic), then more attack packets are blocked, but more normal packets are incorrectly rejected (increased false positives)
Solution Approach 1:
The filter sensitivity is made dynamic rather than static. The system continuously monitors traffic patterns and automatically adjusts the filter threshold based on detected attack characteristics. During normal conditions, the filter operates with lower sensitivity to allow maximum throughput. When an attack is detected, the sensitivity increases automatically to block attack traffic, thus resolving the contradiction between blocking attacks and maintaining normal traffic flow.
Solution Approach 2:
The system changes the parameter (filter threshold) based on traffic conditions. By monitoring packet rates, connection patterns, and other traffic metrics, the system dynamically adjusts the sensitivity parameter to optimize the balance between false positives and false negatives, allowing adaptive response to changing threat levels without manual intervention.
2Measurement precision
If manual operator intervention is used to adjust filter sensitivity, then detection accuracy can be optimized, but the system cannot respond quickly to dynamic traffic conditions and requires longer time scales
Solution Approach 1:
The system performs self-adjustment of filter sensitivity without requiring operator intervention. Automated algorithms monitor traffic patterns, detect attacks in real-time, and automatically adjust filter parameters accordingly. This self-service capability eliminates the time delay associated with manual intervention while maintaining accurate detection, allowing the system to respond immediately to changing traffic conditions and attack patterns.
Solution Approach 2:
The system implements continuous feedback loops where detection results are fed back into the filter adjustment mechanism. Real-time monitoring of traffic characteristics and detection outcomes enables automatic parameter optimization, creating a closed-loop control system that continuously adapts to new conditions without human intervention, thus reducing response time while maintaining precision.
3Device complexity
If a fixed filter parameter is used to block attack packets, then the filter structure is simple, but the system cannot adapt to different attack types and dynamic traffic conditions
Solution Approach 1:
The filter transitions from a static fixed-parameter structure to a dynamic adaptive structure. The system maintains a base filter configuration for simplicity but incorporates automatic parameter adjustment capabilities that activate based on detected traffic patterns. This dynamic approach allows the filter to adapt to different attack types and conditions while maintaining operational simplicity through automated control.
Solution Approach 2:
The filter system is designed to handle multiple attack types and traffic conditions through a single unified adaptive mechanism. Rather than requiring different fixed parameters for different scenarios, the system uses one flexible framework that automatically configures appropriate parameters based on the detected threat, providing universal adaptability across various attack types without increasing structural complexity.
Data Source
AI summary
An apparatus for optimizing a filter based on detected attacks on a data network includes an estimation means and an optimization means. The estimation means operates when a detector detects an attack and the detector transmits an inaccurate attack severity. The estimation means determines an accurate attack severity. The optimization means adjusts a parameter and the parameter is an input to a filter.