ML Traffic Detection Platform for Real-Time Volumetric Attacks
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Solution Overview
Problem
Volumetric attacks overwhelm enterprise organization servers with malicious traffic, leaving no resources for legitimate requests, and existing methods struggle to effectively detect and handle such attacks.
Innovation Solution
A machine learning-based platform trains a model to identify and predict volumetric attacks, execute corrective actions, and update itself based on historical traffic data, using techniques like deep neural networks and supervised learning to distinguish between legitimate and malicious traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning model is trained to detect volumetric attacks, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component between network traffic monitoring and attack response. The model processes traffic data, predicts attack likelihood, and triggers corrective actions, thereby improving detection accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system dynamically adjusts detection parameters by training the machine learning model on historical traffic data and updating it continuously. Hyperparameters are tuned to optimize detection accuracy, and the model adapts to evolving attack patterns, resolving the contradiction between precision and complexity.
2Speed
If corrective actions are executed automatically, then response speed is improved, but loss of information increases
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously learns from historical traffic data and update its predictions. The system monitors the effectiveness of corrective actions and adjusts future responses accordingly, enabling fast automated responses while preserving critical information through continuous learning and adaptation.
3Measurement precision
If machine learning model is updated continuously, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary training of the machine learning model on historical traffic data before deployment. The model is pre-configured with detection parameters and attack patterns, enabling rapid real-time processing. Continuous updates are applied incrementally, balancing accuracy improvement with processing time constraints.
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.