Malicious Content Detection Using Few-Shot Generative 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 classifier capable of recognizing malicious content, which can be easily distributed and updated, allowing for rapid deployment and refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a machine learning classifier is developed quickly using a small number of examples, then the development time and resource investment are reduced, but the sensitivity and robustness of the classifier deteriorate
Solution Approach 1:
A large pre-trained language model is prepared in advance through extensive training on general language data before being adapted for specific malicious content detection tasks. This preliminary training establishes a robust foundation that enables rapid task adaptation with minimal additional training data and time.
Solution Approach 2:
The classifier development process is divided into two distinct phases: (1) a preliminary pre-training phase using large general language datasets to build foundational capabilities, and (2) a rapid task-specific adaptation phase using small targeted datasets for malicious content detection. This segmentation allows the model to leverage pre-acquired knowledge while quickly adapting to new threats.
2Reliability
If a machine learning classifier uses more resources and takes more time to run, then the detection accuracy and robustness improve, but the computational efficiency and deployment speed deteriorate
Solution Approach 1:
The essential detection capabilities are extracted from a large pre-trained language model and applied to specific malicious content detection tasks. By removing unnecessary complexity and focusing on task-relevant features, the system achieves high detection accuracy with reduced computational overhead and faster deployment.
Solution Approach 2:
The model parameters are optimized by adjusting the amount of training data and computational resources allocated to different training phases. By changing parameters such as training dataset size, model architecture scale, and training duration, the system achieves an optimal balance between detection accuracy and deployment efficiency for different threat scenarios.
3Reliability
If extensive research and analysis are conducted to develop a machine learning model, then the model reliability and performance improve, but the development time and ease of deployment deteriorate
Solution Approach 1:
The system uses a pre-trained language model as a template that can be rapidly copied and adapted for different malicious content detection tasks. Instead of developing new models from scratch for each threat, the same foundational model is copied and fine-tuned with task-specific data, dramatically simplifying deployment while maintaining high performance through the proven pre-training process.
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.