LLM Verification for Network Threat Detection Ground Truth
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Solution Overview
Problem
Traditional cybersecurity measures, including signature-based detection and rule-based systems, struggle to identify and mitigate evolving cyber threats in real-time due to limitations in machine learning (ML) models, which often produce false positives or false negatives and fail to contextualize classifications.
Innovation Solution
Integrate a Large Language Model (LLM) as an additional layer of verification for ML model predictions, leveraging its ability to analyze contextual information and provide insights, enhancing threat detection accuracy by confirming or contesting initial classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If ML models are used for threat detection, then detection capability is improved, but false positives and false negatives increase
Solution Approach 1:
The patent introduces LLMs as intermediary verification layers between network traffic and threat detection systems. These LLMs analyze traffic patterns, protocol compliance, and contextual information to verify ML model predictions, reducing false positives and false negatives by providing an additional validation step that contextualizes classifications
Solution Approach 2:
The system implements feedback loops where LLM verification results are used to continuously improve and refine threat detection accuracy. The LLMs provide contextual feedback on ML model predictions, enabling the system to learn from verification outcomes and adjust detection parameters to reduce erroneous classifications over time
2Productivity
If ML models provide predictions, then threat detection speed is improved, but contextualization capability deteriorates
Solution Approach 1:
LLMs serve as intermediary layers that add contextual information to ML model predictions without significantly impacting detection speed. The LLMs analyze protocol compliance, traffic patterns, and contextual cues to enrich predictions with meaningful context while maintaining the rapid processing capabilities of the underlying ML models
Solution Approach 2:
The system adds a contextual dimension to threat detection by introducing LLM-based analysis that operates alongside traditional ML prediction. This additional dimension provides semantic understanding and contextualization of traffic patterns, transforming raw predictions into contextually-rich classifications without sacrificing detection throughput
Data Source
AI summary
The present application discloses a method, system, and computer system for enriching a ground truth of a machine learning-based detection using a large language model (LLM). The method includes: (a) obtaining a machine learning (ML)-based prediction for a security detection, (b) prompting a large language model (LLM) for an LLM-based prediction for the security detection based at least in part on a set of examples of malware, and (c) determining a ground truth of the ML-based prediction for the security detection based at least in part on a response from the LLM.