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

arXiv: https://arxiv.org/abs/2508.18148 · HTML: https://mlsanity.com/paper/2508.18148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 66 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [Long Text Generation via Adversarial Training with Leaked Information](https://mlsanity.com/paper/1709.08624/) (2017)
- [Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization](https://mlsanity.com/paper/2008.01403/) (2020)
- [On the Effectiveness of Large Language Models in Domain-Specific Code Generation](https://mlsanity.com/paper/2312.01639/) (2023)
- [Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval](https://mlsanity.com/paper/2209.11572/) (2022)
- [You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos](https://mlsanity.com/paper/2303.07863/) (2023)
- [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https://mlsanity.com/paper/1702.07983/) (2017)
- [Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat](https://mlsanity.com/paper/2011.10254/) (2020)
- [ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering](https://mlsanity.com/paper/2011.10331/) (2020)
- [Restricted Black-box Adversarial Attack Against DeepFake Face Swapping](https://mlsanity.com/paper/2204.12347/) (2022)
- [Double Self-weighted Multi-view Clustering via Adaptive View Fusion](https://mlsanity.com/paper/2011.10396/) (2020)
- [The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training](https://mlsanity.com/paper/2211.00525/) (2022)
- [Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution Detection in Class-Imbalanced Data](https://mlsanity.com/paper/2412.06284/) (2024)
- [Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks](https://mlsanity.com/paper/2202.07261/) (2022)
- [A survey on text generation using generative adversarial networks](https://mlsanity.com/paper/2212.11119/) (2022)
- [Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network](https://mlsanity.com/paper/2412.15678/) (2024)
- [Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection](https://mlsanity.com/paper/2409.09953/) (2024)
- [3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack](https://mlsanity.com/paper/2308.07546/) (2023)

## Abstract (arXiv)

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 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.