Network DoS Detection Pipeline for Fast, Accurate Mitigation
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Solution Overview
Problem
Existing mitigation solutions for Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are slow and inefficient, often exceeding the duration of the attack, and conventional methods like blacklisting IP addresses are ineffective due to dynamic IP assignments and the difficulty in differentiating between legitimate and malicious traffic, especially in high-rate and low-rate attacks.
Innovation Solution
A two-stage authentication protocol pipeline that includes a high-rate DoS attack coarse detection module and a low-rate DoS attack fine detection module, utilizing various algorithms such as statistical, machine learning, and behavior analysis to identify and mitigate DoS attacks by clustering connection requests and adjusting computational resources based on confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mitigation solutions are used, then DoS attacks can be detected, but the response time is too slow and exceeds the attack duration
Solution Approach 1:
The detection system is divided into multiple stages: initial detection phase using lightweight algorithms, intermediate analysis phase for suspicious traffic, and deep inspection phase for confirmed attacks. This segmentation allows the system to quickly filter out normal traffic while dedicating more resources to potential threats, achieving both speed and accuracy.
Solution Approach 2:
The system performs preliminary actions by establishing baseline traffic patterns and detection rules before attacks occur. During normal operation, the system pre-processes traffic data and maintains ready-to-activate mitigation protocols, enabling immediate response when attacks are detected without waiting for full analysis.
2Object-affected harmful factors
If simple blacklisting of IP addresses is used, then blocking harmful traffic is achieved, but legitimate devices are also blocked due to dynamic IP assignments
Solution Approach 1:
Instead of applying a uniform blacklisting policy to all traffic from an IP address, the system applies different quality levels of inspection and blocking to different packets and time periods. Legitimate traffic from dynamic IPs receives verification through behavioral analysis, while confirmed malicious traffic receives immediate blocking, allowing the system to adapt its response to the specific nature of each connection attempt.
Solution Approach 2:
The system changes parameters such as inspection depth, blocking duration, and threshold values based on the specific characteristics of each connection attempt. Rather than permanently blocking an IP address, the system adjusts blocking parameters dynamically based on traffic patterns, device behavior, and attack signatures, reducing false positives while maintaining protection.
3Measurement precision
If high computational resource algorithms are used for attack detection, then detection accuracy is improved, but the computational cost makes them unsuitable for standard mitigation operations
Solution Approach 1:
The system applies partial computational resources to all traffic and excessive (full) computational resources only to suspicious traffic. Normal traffic receives minimal processing using simple rules and heuristics, while traffic that triggers detection thresholds receives intensive analysis using machine learning algorithms and behavioral analysis, optimizing the balance between accuracy and resource consumption.
Solution Approach 2:
The computational resources allocated to traffic analysis are dynamic rather than static. The system adjusts the level of analysis based on current traffic conditions, threat levels, and detected anomalies. During normal operation, minimal resources are used, but when attacks are detected or suspected, the system dynamically increases computational power and analysis depth to maintain high detection accuracy.
Data Source
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AI summary
Methods and systems for detecting Denial-of-Service (DoS) attacks on a network. The method includes clustering connection requests received at the network during a first collection time having a first time span based on communication datasets extracted therefrom. For at least one connection cluster, a first DoS attack detection algorithm is executed to determine a first probability that the connection cluster is associated with a DoS attack. The first confidence score is compared with a first threshold to determine whether a mitigation order is to be generated. The connection cluster is adjusted by extracting second communication datasets from connection requests received at the network during a second collection time having a second time span longer than the first time span, and a second DoS attack detection algorithm is executed to determine a second probability that the connection cluster is associated with a DoS attack.