DPU-Based ML DDoS Detection With Hardware Rule Enforcement
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Solution Overview
Problem
Conventional signature-based detection systems for network attacks, such as DDOS, are ineffective against evolving cyber threats due to the need for continuous signature updates and are time-consuming and expensive, failing to detect new attacks promptly.
Innovation Solution
An AI/ML-based DDOS detection and mitigation solution using a DPU's acceleration hardware engine that extracts features from network traffic and metadata to identify anomalies and patterns in real-time, enabling quick response and adaptation to new attack methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based detection systems are used to detect network attacks, then detection capability for known attacks is maintained, but the system becomes time-consuming and expensive to update continuously, and fails to detect new attacks promptly
Solution Approach 1:
The patent replaces the manual signature-based detection mechanism with an automated machine learning-based detection system. The ML model automatically learns attack patterns from network traffic data, eliminating the need for manual signature updates while maintaining detection capability for both known and new attacks.
Solution Approach 2:
The detection system performs self-updating through continuous learning from network traffic data. The ML model automatically adapts to new attack patterns without requiring external intervention for signature updates, making the system self-maintaining and responsive to evolving threats.
2Reliability
If signature-based detection systems are used to detect network attacks, then detection capability for known attacks is maintained, but the system becomes expensive to operate due to continuous updates
Solution Approach 1:
The patent replaces the expensive manual signature update process with an automated machine learning system that learns from network traffic data. This substitution eliminates the need for continuous purchase and deployment of signature updates, significantly reducing operational costs while maintaining detection effectiveness.
Solution Approach 2:
The system reduces operational costs by performing self-learning and self-updating through automated ML training on network traffic data, eliminating the need for expensive external signature update services and manual intervention.
3Productivity
If AI/ML-based detection system is implemented using DPU acceleration hardware engine, then detection speed and accuracy for new attacks is improved, but device complexity increases
Solution Approach 1:
The patent introduces a DPU (Data Processing Unit) as an intermediary hardware component specialized for accelerating ML inference. The DPU offloads complex ML detection computations from the main CPU, enabling fast detection of new attacks while keeping the overall system architecture manageable through hardware abstraction.
Solution Approach 2:
The patent segments the detection system into distinct functional components: network traffic collection, feature extraction, ML inference on DPU, and response execution. This modular segmentation allows each component to be optimized independently, managing overall system complexity while achieving high detection speed.
4Measurement precision
If AI/ML-based detection system is implemented using DPU acceleration hardware engine, then detection accuracy and ability to handle large volumes of data is improved, but device complexity and implementation difficulty increases
Solution Approach 1:
The DPU serves as a specialized intermediary hardware device that simplifies the implementation of complex ML detection algorithms. By providing dedicated ML inference capabilities, the DPU abstracts away the complexity of implementing and optimizing ML models, allowing accurate detection while reducing implementation burden.
Solution Approach 2:
The DPU provides universal ML inference capabilities that can handle various detection algorithms and models through a standardized interface. This multi-functionality allows the system to achieve high detection accuracy across different attack types without requiring separate implementation for each algorithm, reducing overall implementation complexity.
Data Source
AI summary
Apparatuses, systems, and techniques for detecting that a host device is subject to a distributed denial of service (DDOS) attack using a machine learning (ML) detection system are described. A computing system includes a data processing unit (DPU) with a network interface and a hardware-acceleration engine. The DPU hosts a hardware-accelerated security service to extract features from network data and metadata from the hardware acceleration engine and sends the extracted features to the ML detection system. The ML detection system determines whether the host device is subject to a DDOS attack using the extracted features. The ML detection system can send an enforcement rule to the hardware-acceleration engine responsive to a determination that the host device is subject to the DDOS attack.