Rapid Malicious Content Detector Development With Few-Shot Language Models
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Solution Overview
Problem
Existing cybersecurity systems face challenges in rapidly developing and deploying machine learning classifiers for detecting malicious content, making it difficult for analysts to create, share, and improve models within cybersecurity infrastructure.
Innovation Solution
A generative natural language model is used with a small number of selected text content samples to quickly develop a malicious content detector, which can classify target content as malicious or benign, enabling rapid deployment and refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning classifier is developed with extensive research and analysis using traditional methods, then the classifier achieves high sensitivity and robustness, but the development time and deployment complexity increase significantly
Solution Approach 1:
The patent uses a pre-trained generative language model as an intermediary that has already learned general language patterns and malicious content characteristics from extensive training data. This intermediary model can be quickly adapted to specific detection tasks through few-shot learning, avoiding the need to train from scratch while maintaining high detection accuracy. The pre-trained model serves as a bridge between general language understanding and specific malicious content detection.
Solution Approach 2:
The system performs preliminary action by pre-training the generative language model on large datasets before deployment. The model is pre-adapted to understand malicious content patterns through few-shot examples provided during inference. This preliminary preparation enables rapid deployment for new threat types without requiring extensive retraining, resolving the contradiction between preparation time and detection accuracy.
2Productivity
If a machine learning classifier is developed quickly using a small number of examples, then the deployment speed increases, but the classifier sensitivity and robustness decrease
Solution Approach 1:
The system applies partial action by using only a few representative examples (few-shot learning) instead of requiring large training datasets. The pre-trained generative model has already learned general patterns from extensive prior training, so only minimal additional examples are needed for specific detection tasks. This partial approach achieves rapid deployment while maintaining adequate detection accuracy for the specific threat type.
Solution Approach 2:
The system changes parameters by adjusting the few-shot examples and task descriptions provided to the generative model during inference. By carefully selecting representative examples and crafting precise task descriptions, the model adapts to new threat types with high accuracy using minimal data. This parameter adjustment approach enables fast adaptation without sacrificing detection reliability.
3Reliability
If traditional machine learning models are used for malicious content detection, then the models are deeply embedded and require significant development resources, but this makes it difficult to rapidly create and share models within cybersecurity infrastructure
Solution Approach 1:
The patent implements universality by using a single pre-trained generative language model that can be adapted to detect multiple types of malicious content through few-shot learning. The same model infrastructure handles phishing, malware, spam, and other threat types by simply changing the few-shot examples and task descriptions. This universal approach eliminates the need to develop and maintain separate specialized models for each threat type, reducing development complexity while maintaining detection accuracy.
4Loss of time
If a rapidly developed classifier using generative models is deployed, then initial protection against new threats is provided quickly, but the classifier may use more resources and take more time to run compared to traditionally developed models
Solution Approach 1:
The system performs preliminary action by pre-training the generative language model on large datasets before deployment. This extensive pre-training is done once, and the resulting model can be rapidly adapted to new threat types through few-shot learning with minimal additional computational resources. The heavy computational work is front-loaded during pre-training, enabling fast, resource-efficient adaptation to new threats.
Data Source
AI summary
Methods and systems are described for developing a malicious content detector to identify new malicious text content, such as phishing messages, malicious documents, and/or malicious web content. A computing device is used to generate input data which contains an instruction, examples of content, and content to be analyzed. The examples include malicious and benign content samples, designed to recognize similar malicious content. The computing device feeds this input into a generative language model, which produces text labels that indicate the maliciousness of the content to be analyzed. The methods and systems enable rapid development of security protection by leveraging a small number of malicious samples, instead of training with a large dataset of new training samples.