Adaptive Network Anomaly Thresholds for DDoS Detection
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Solution Overview
Problem
Existing network monitoring systems face challenges in accurately detecting DDoS attacks due to user-defined thresholds leading to false positives or negatives, and manual threshold adjustments are resource-intensive and prone to attacker tactic shifts.
Innovation Solution
A system that automatically generates and adjusts detection thresholds using historical traffic data, allowing users to input or adjust suggested values for improved anomaly detection in communications networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual threshold configuration is used for anomaly detection, then detection precision can be adjusted, but operational complexity and resource consumption increase significantly
Solution Approach 1:
The system automatically generates and adjusts detection thresholds using historical traffic data without requiring manual configuration. The machine learning model analyzes past network behavior to autonomously determine optimal threshold values, eliminating the need for operators to manually set parameters while maintaining high detection precision.
Solution Approach 2:
The system dynamically adjusts detection thresholds based on changing network conditions and attack patterns. The machine learning model continuously learns from new data to modify threshold parameters automatically, allowing the system to adapt to evolving threats without manual reconfiguration.
2Device complexity
If static thresholds are used for anomaly detection, then system simplicity is maintained, but adaptability to changing attack methodologies deteriorates
Solution Approach 1:
The system transitions from static thresholds to dynamic, adaptive thresholds that automatically adjust based on learned patterns from historical data. The machine learning model continuously updates detection parameters in response to changing network conditions and emerging attack tactics, maintaining simplicity while achieving high adaptability.
Solution Approach 2:
The system implements feedback mechanisms where detection results and network behavior data are fed back into the machine learning model. This feedback loop enables the system to learn from past detections and continuously improve its threshold accuracy, enhancing adaptability to new attack methodologies without increasing operational complexity.
3Measurement precision
If multiple thresholds are manually configured for different network characteristics, then detection accuracy improves, but time consumption for configuration increases
Solution Approach 1:
The system performs preliminary analysis of historical traffic data to pre-determine optimal threshold values before actual detection operations. The machine learning model processes past network behavior in advance to establish configuration-ready thresholds, eliminating the need for operators to spend time manually configuring multiple parameters while maintaining high detection accuracy.
Data Source
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AI summary
The present disclosure describes a method for detecting and mitigating network attacks. The method includes collecting network data packets transmitted by a plurality of computing devices across a communications network; presenting a user interface on a user device, the user interface comprising a threshold calculation button and one or more fields each corresponding to a threshold for a different network characteristic of the communications network; receiving a selection of the threshold calculation button from the user device; determining a threshold for each of the one or more fields based on the collected network data packets; responsive to receiving the selection of the threshold calculation button, automatically populating each of the one or more fields with the threshold determined for the field; and detecting an attack on the communications network using a first threshold that was automatically populated into a first field of the one or more fields.