Network Monitoring Device DoS Detection Configuration
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Solution Overview
Problem
Internet Service Providers face challenges in detecting and mitigating Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks, as the source of these attacks is often difficult to determine due to forged source addresses, and existing detection systems require manual configuration and trial-and-error tuning of threshold settings, especially in high-speed networks with limited resources.
Innovation Solution
A method and system for configuring network monitoring devices that analyze historical network traffic flow information to automatically set threshold values based on identified characteristic metrics, enabling timely detection of DoS and DDoS attacks and reducing the need for manual tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration and trial-and-error tuning of threshold settings are used, then detection accuracy can be achieved, but configuration complexity and time consumption increase significantly
Solution Approach 1:
The system automatically configures threshold settings by analyzing historical network traffic data and identifying characteristic values, eliminating the need for manual configuration and trial-and-error tuning. The monitoring device performs self-configuration based on learned patterns from historical malicious activity data
Solution Approach 2:
The system pre-processes historical network traffic flow information to identify characteristic values and establish baseline thresholds before actual DoS detection begins. This preliminary analysis phase prepares the detection system with pre-configured thresholds based on historical patterns, enabling faster and more accurate detection without manual intervention during operation
2Measurement precision
If manual configuration of threshold settings is performed, then detection specificity can be improved, but time consumption and operational overhead increase
Solution Approach 1:
The monitoring device automatically determines appropriate threshold values by analyzing historical network traffic data and identifying characteristic patterns of malicious activity. This self-configuration process eliminates manual tuning time while maintaining detection specificity through data-driven threshold selection
Solution Approach 2:
The system uses historical network traffic flow information as feedback to automatically adjust and configure threshold settings. By learning from past malicious activity patterns stored in the database, the system refines its threshold values to improve detection specificity without requiring manual configuration time
3Speed
If network monitoring analyzes all traffic in real-time, then detection timeliness is improved, but resource consumption increases
Solution Approach 1:
The system pre-analyzes historical network traffic data to establish baseline thresholds and characteristic values before real-time monitoring begins. This preliminary processing phase creates reference patterns that enable faster real-time detection by comparing current traffic against pre-established benchmarks rather than analyzing all traffic from scratch
Solution Approach 2:
The system dynamically adjusts monitoring parameters and threshold values based on historical traffic patterns and identified characteristic values. By changing the parameters of what to monitor and at what thresholds, the system optimizes resource consumption while maintaining detection speed through adaptive parameter selection based on learned traffic characteristics
Data Source
AI summary
A method for configuring a network monitoring device is provided. One or more performance metrics associated with one or more thresholds to be configured are received from a user. Historical network traffic flow information associated with a previously detected malicious activity is analyzed to identify characteristic values for the one or more performance metrics. Threshold values are automatically configured based on the identified characteristic values.