Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation

• URL: http://arxiv.org/abs/2508.18148v1
• Date: Mon, 25 Aug 2025 15:55:17 GMT
• Title: Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
• Authors: Haijian Ma, Daizong Liu, Xiaowen Cai, Pan Zhou, Yulai Xie,
• Abstract summary: Intrusion Detection Systems (IDS) play a crucial role in network security defense.<n>A significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples.<n>This paper introduces a novel semi-supervised framework textbfGANGRL-LLM, which integrates Generative Adrial Networks (GANs) with Large Language Models (LLMs)
• Score: 47.76793060077816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intrusion Detection Systems (IDS) play a crucial role in network security defense. However, a significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples. To address these issues, this paper introduces a novel semi-supervised framework \textbf{GANGRL-LLM}, which integrates Generative Adversarial Networks (GANs) with Large Language Models (LLMs) to enhance malicious code generation and SQL Injection (SQLi) detection capabilities in few-sample learning scenarios. Specifically, our framework adopts a collaborative training paradigm where: (1) the GAN-based discriminator improves malicious pattern recognition through adversarial learning with generated samples and limited real samples; and (2) the LLM-based generator refines the quality of malicious code synthesis using reward signals from the discriminator. The experimental results demonstrate that even with a limited number of labeled samples, our training framework is highly effective in enhancing both malicious code generation and detection capabilities. This dual enhancement capability offers a promising solution for developing adaptive defense systems capable of countering evolving cyber threats.

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