ML Traffic Classification for Volumetric Attack Mitigation
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Solution Overview
Problem
Existing systems struggle to effectively distinguish between legitimate and malicious traffic in volumetric attacks, leading to overwhelmed server resources and inability to process legitimate requests.
Innovation Solution
A machine learning-based platform that trains a model to identify and predict volumetric attacks, executes corrective actions, and updates based on historical traffic data, using a machine learning model to differentiate between legitimate and malicious traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional traffic filtering methods are used, then system simplicity is maintained, but the ability to distinguish between legitimate and malicious traffic deteriorates
Solution Approach 1:
A machine learning-based traffic classification system is introduced as an intermediary between network traffic and server resources. The system analyzes traffic patterns and makes intelligent decisions about which requests to allow or block, improving classification accuracy without requiring complex traditional filtering mechanisms at the server level.
Solution Approach 2:
Traditional mechanical traffic filtering methods are replaced with a machine learning-based system that uses algorithms to analyze traffic patterns. The system substitutes rule-based filtering with predictive modeling, enabling more accurate distinction between legitimate and malicious traffic.
2Productivity
If volumetric attack traffic is not filtered, then legitimate requests can be processed, but server resources are overwhelmed
Solution Approach 1:
The system performs preliminary analysis of traffic patterns before requests reach the server. By detecting volumetric attack characteristics in advance and filtering them out, the system prevents server resources from being overwhelmed while maintaining the ability to process legitimate requests efficiently.
Solution Approach 2:
The machine learning system continuously learns from traffic patterns and adjusts its classification decisions. Feedback from observed traffic behavior allows the system to improve its ability to distinguish between legitimate and malicious traffic, dynamically adapting to new attack patterns while maintaining high request processing capability.
3Object-affected harmful factors
If aggressive traffic blocking is implemented, then malicious traffic is reduced, but legitimate requests may be incorrectly blocked
Solution Approach 1:
Aggressive rule-based blocking is replaced with machine learning-based intelligent classification. The system uses patterns recognition and predictive modeling to make more accurate distinctions, reducing false positives while maintaining effective malicious traffic reduction.
Solution Approach 2:
The system changes the parameters used for traffic classification from simple threshold-based rules to complex machine learning models that analyze multiple traffic characteristics. This allows for more nuanced decision-making that reduces both malicious traffic and false blocking of legitimate requests.
Data Source
AI summary
Aspects related to a machine learning-based platform to detect and handle volumetric attacks are provided. A volumetric attack detection and handling platform may train a machine learning model to identify and/or predict volumetric attacks, generate predicted corrective actions, and execute actual corrective actions. The platform may receive information of a network request corresponding to a volumetric attack or a request from a legitimate user. The platform may identify a correlation of volumetric attack and/or legitimate requests using the model. The platform may further identify a predicted corrective action using the model. The platform may cause, based on identifying the predicted corrective, initiation of a response to the malicious traffic request. The response to the malicious traffic request may comprise implementing an actual corrective action generated by the model. The platform may update the machine learning model based on the information of recent requests and corrective actions.