Security-Specific LLM Training for Semantic Threat Detection
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Solution Overview
Problem
Existing machine learning systems struggle with accurately generating and detecting semantically related security information due to the distinct language used in cybersecurity logs and threat intelligence documents, leading to inefficiencies in energy consumption and resource costs.
Innovation Solution
Training a security-specific large language model (LLM) with security-specific datasets and objectives, incorporating similarity deduplication and fine-tuning to enhance the model's ability to capture semantic meaning in security-related data, enabling improved anomaly detection and search functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a security-specific large language model is trained and deployed to generate and detect security information, then the accuracy of anomaly detection and search capabilities is improved, but the energy consumption and computing resource costs increase
Solution Approach 1:
The security-specific large language model is pretrained offline with security-specific datasets and objectives before deployment. This preliminary training action enables the model to capture semantic meaning in security-related data in advance, so that during actual anomaly detection and search operations, the model can quickly process queries without requiring extensive real-time computational resources, thus improving detection accuracy while controlling energy consumption during operational use
2Measurement precision
If a security-specific large language model is trained and deployed to generate and detect security information, then the accuracy of anomaly detection and search capabilities is improved, but the cost of computing and storage resources increases
Solution Approach 1:
The model is pretrained offline with security-specific datasets and objectives before deployment, enabling it to capture semantic meaning in security-related data in advance. This preliminary action reduces the computational burden during operational use, as the model has already learned security-specific patterns and relationships, thereby improving detection accuracy while reducing real-time computing and storage resource requirements
Solution Approach 2:
The security-specific large language model captures and stores semantic representations of security events, log lines, and threat intelligence documents in its trained parameters. This copying of security knowledge into the model's structure enables efficient retrieval and comparison during anomaly detection, reducing the need for extensive storage and computation of raw security data during operational phases
Data Source
AI summary
Methods and apparatuses for improving the performance and energy efficiency of machine learning systems that generate security specific machine learning models and generate security related information using security specific machine learning models are described. A security specific machine learning model may comprise a security specific large language model (LLM). The security specific LLM may be trained and deployed to generate semantically related security information. The security specific LLM may be pretrained with a security specific data set that was generated using similarity deduplication and long line handling, and with security specific objectives, such as next log line prediction based on host, system, application, and cyber attacker behavior. The security specific large language model may be fine-tuned using a security specific similarity dataset that may be generated to align the security specific LLM to capture similarity between different security events.