LLM Cyberattack Signature Generation with Contextual Knowledge
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Solution Overview
Problem
Existing cyberattack signature generation methods are labor-intensive, prone to obsolescence, and lack the sophistication to detect variants of cyberattacks, especially when relying on manual signature generation and generic regular expression-based methods.
Innovation Solution
A context-based cyberattack signature generation system utilizing large language models (LLMs) that incorporate case knowledge and domain knowledge to generate cyberattack signatures, with a signature testing module performing validation and traffic testing to ensure the generated signatures are effective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual signature generation by domain-level experts is used, then signature quality can be maintained, but labor cost and time consumption increase significantly
Solution Approach 1:
The patent introduces an intermediary system consisting of a language model and prompt engineering framework that mediates between domain knowledge and signature generation. The system uses carefully designed prompts incorporating case knowledge and domain knowledge to guide the language model in generating high-quality signatures without requiring manual expert intervention for each signature.
Solution Approach 2:
The system enables self-service signature generation by automatically processing case knowledge and domain knowledge through the language model. The automated pipeline includes validation testing and traffic testing components that allow the system to self-evaluate and refine generated signatures without continuous manual oversight.
2Extent of automation
If generic regular expression-based methods are used for signature generation, then automation can be achieved, but detection capability for cyberattack variants is insufficient
Solution Approach 1:
The patent changes the fundamental parameters of signature generation by transitioning from fixed regular expression patterns to dynamic language model-based generation. The system adjusts parameters such as context understanding, pattern recognition, and adaptive signature creation by incorporating case knowledge and domain knowledge into the generation process.
Solution Approach 2:
The system combines multiple knowledge sources (case knowledge, domain knowledge) with advanced language modeling capabilities to create a composite approach to signature generation. This composite methodology integrates structured prompt schemas with unstructured knowledge bases to achieve both automation and high detection capability.
3Reliability
If existing cyberattack signatures are periodically updated manually, then signatures can respond to evolving attacks, but labor cost accumulates over time
Solution Approach 1:
The patent implements continuous signature generation and updating through an automated pipeline that continuously processes new case knowledge and domain knowledge. The system maintains continuous improvement of signatures through automated validation testing and traffic testing, eliminating the periodic manual update cycle.
Solution Approach 2:
The system performs preliminary action by pre-processing and organizing domain knowledge and case knowledge into structured formats that the language model can efficiently process. Prompt schemas are pre-engineered to extract relevant information from knowledge sources, enabling rapid signature generation when new threats emerge.
Data Source
AI summary
A context-based cyberattack signature generation system (“signature generation system”) disclosed herein comprises a signature prompt schema for generating prompts to a language model that generates cyberattack signatures. The signature prompt schema comprises a description of syntax for the cyberattack signatures and descriptions of case knowledge and domain knowledge for a type of cyberattack corresponding to a cyberattack signature. The signature generation system tests cyberattack signatures generated with the signature prompt schema against minimum signature conditions and traffic with ground-truth malicious/benign labels. Once the signature prompt schema passes the tests, the signature generation system deploys the tested signature prompt schema in combination with the language model for cyberattack signature generation.