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Related Experiment Video

Updated: Sep 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Published on: December 6, 2024

Optimization for threat classification of various data types-based on ML model and LLM.

Chaerim Hong¹, Taeyeon Oh²

¹Seoul AI School, aSSIST University, Seoul, 03767, South Korea.

Scientific Reports

|July 2, 2025

Summary

This study integrates machine learning (ML) and large language models (LLM) to detect cybersecurity threats by analyzing security news and source code. The framework effectively identifies vulnerabilities, improving detection accuracy and reducing false positives.

Keywords:

Coode weakness Data bias Large language models Machine learning Post news Security weakness

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Area of Science:

Cybersecurity
Artificial Intelligence
Software Engineering

Background:

The rapid advancement of AI technology has led to an increase in sophisticated cybersecurity threats.
Existing security tools struggle to effectively detect and respond to these evolving threats.
There is a growing need for integrated systems capable of analyzing diverse data sources for threat detection.

Purpose of the Study:

To explore the integral analysis of heterogeneous data, including security threat news and source code weaknesses, within a unified ML and LLM environment.
To develop and evaluate an effective security threat detection system leveraging both ML and LLM capabilities.
To improve the accuracy and efficiency of vulnerability detection in source code.

Main Methods:

Applied scaling and normalization to Post News data to mitigate bias.
Utilized syntax analysis, semantic analysis, and data flow information for integrated source code analysis.
Developed a systematized data labeling and formatting approach for both ML and LLM application.
Employed Abstract Syntax Trees (AST) for feature extraction in source code analysis.

Main Results:

ML models (XGBoost, SVM, Random Forest) achieved F1-scores of 0.96-0.97 on post-news data.
LLM models (ST5-xxl, XLNet, BERT, CodeBERT, GraphCodeBERT) achieved an F1-score of 0.97 on post-news data.
LLM models (ST5-xxl, XLNet, CodeBERT, GraphCodeBERT) achieved near-perfect accuracy (0.9999) in C/C++ weakness detection.
TF-IDF embedding method outperformed Word2Vec for ML models in source code analysis.

Conclusions:

The proposed integrated ML and LLM framework effectively detects source code vulnerabilities using AST.
The framework overcomes limitations of traditional static analysis tools by considering code structure and semantics.
Combining AST-based features with LLM capabilities enhances generalization for new vulnerabilities and reduces false positives.