LLM Packet Embedding Detection for Adaptive DDoS Traffic Filtering
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Solution Overview
Problem
Conventional DDoS attack detection methods rely on manually set thresholds that are not adaptable to rapidly changing network environments, leading to inefficiencies in detecting and mitigating malicious network traffic.
Innovation Solution
A large language model-based embedder generates embeddings from network packets, which are clustered and analyzed for entropy, with a loss value computed to refine the model, allowing it to dynamically adapt to varying attack patterns and distinguish between malicious and non-malicious packets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manually determined thresholds are used for monitoring indicators, then the detection system is simple to implement, but it cannot adapt to rapidly changing network environments
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously monitoring network traffic patterns and automatically adapting detection thresholds based on observed baseline behavior. The system transitions from static manual thresholds to dynamic thresholds that evolve with network conditions, resolving the contradiction between adaptability and complexity through automated adaptation mechanisms.
Solution Approach 2:
The detection system performs self-service by automatically establishing baselines and adjusting thresholds without manual intervention. The system monitors its own performance, learns from traffic patterns, and autonomously adapts to changing network environments, eliminating the need for manual threshold determination while maintaining simplicity of operation.
2Measurement precision
If conventional threshold-based detection methods are used, then the system is easy to operate, but detection accuracy deteriorates in complex attack scenarios
Solution Approach 1:
The patent replaces manual threshold-setting mechanisms with automated machine learning-based detection. The system uses algorithms to automatically analyze traffic patterns, establish baselines, and determine thresholds, substituting mechanical/manual operations with intelligent automated systems that improve accuracy while maintaining ease of operation through black-box automation.
Solution Approach 2:
The system implements continuous feedback loops where detection results are fed back into the learning mechanism, allowing the system to refine its accuracy over time. The automated system monitors its own performance and adjusts its detection parameters based on observed patterns, improving measurement precision while requiring minimal operational intervention.
3Adaptability or versatility
If dynamic adaptation methods are implemented, then the system can respond to changing attack patterns, but computational resources increase
Solution Approach 1:
The patent applies partial adaptation by focusing computational resources on the most critical detection tasks and high-risk traffic patterns. The system dynamically adjusts its level of analysis based on threat likelihood, performing comprehensive analysis only when necessary and using lighter monitoring for normal traffic, thereby reducing overall computational resource consumption while maintaining adaptability.
Data Source
AI summary
A method and system for detecting malicious network packets via an intelligent large language model are described. In addition, a method for fine-tuning a pretrained large language model to detect malicious network packets is described. The training process generates a plurality of embeddings from input network packet data, generates clusters for those embeddings, performs an entropy analysis, and calculates a loss value. Contrastive learning is used to further fine-tune the large language model based embedder.