Mingrui Ma

Mingrui Ma

Publications and Preprints

You can also find my articles on my Google Scholar profile.

Research and application of artificial intelligence based webshell detection model: A literature review

Published in Arxiv Preprint, 2024

TLDR: This article provides a detailed summary and synthesis of webshell detection solutions based on artificial intelligence technology, analyzes the shortcomings of existing solutions, and predicts the future development trends in the related field.

Recommended citation: Ma M, Han L, Zhou C. Research and application of artificial intelligence based webshell detection model: A literature review[J]. arXiv preprint arXiv:2405.00066, 2024. https://arxiv.org/abs/2405.00066

Research and application of Transformer based anomaly detection model: A literature review

Published in Arxiv Preprint, 2024

TLDR: This article offers a detailed summary, synthesis, and analysis of transformer-based research and applications in the field of anomaly detection. To the best of our knowledge, this is also the first comprehensive review that focuses on the research related to Transformer in the context of anomaly detection.

Recommended citation: Ma M, Han L, Zhou C. Research and application of Transformer based anomaly detection model: A literature review[J]. arXiv preprint arXiv:2402.08975, 2024. https://arxiv.org/abs/2402.08975

Large language models are few-shot generators: Proposing hybrid prompt algorithm to generate webshell escape samples

Published in Arxiv Preprint, 2024

TLDR: This article introduces the Hybird Prompt algorithm, which combines the advantages of various prompt algorithms such as CoT, ToT, and SC. It generates webshell samples with high Escape Rate and Survival Rate through various components and hierarchical reasoning steps.

Recommended citation: Ma M, Han L, Zhou C. Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples[J]. arXiv preprint arXiv:2402.07408, 2024. https://arxiv.org/abs/2402.07408

BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data

Published in Advanced Engineering Informatics, 2023

TLDR: This article introduces the Bi-Transformer anomaly detection method for anomaly detection in multivariate time series data, and proposes corresponding enhancements such as an adaptive multi-head attention mechanism and a modified Decoder structure to further improve the performance of BTAD. Experimental results on multiple mainstream multivariate time series datasets demonstrate that BTAD exhibits outstanding overall anomaly detection performance.

Recommended citation: Ma M, Han L, Zhou C. BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data[J]. Advanced Engineering Informatics, 2023, 56: 101949. http://jkpathfinder.github.io/files/paper3.pdf

Detection method for c language family based on graph neural network and generic vulnerability analysis framework

Published in Netinfo Security, 2022

TLDR: This paper proposes CSVDM for static vulnerability detection tasks of C family languages. By combining Graph Neural Network with Generic Vulnerability Analysis Framework, CSVDM can perform high-precision multi-class classification tasks.

Recommended citation: ZHU Lina, MA Mingrui, ZHU Dongzhao. Detection Method for C Language Family Based on Graph Neural Network and Generic Vulnerability Analysis Framework[J]. Netinfo Security, 2022, 22(10): 59-68. http://jkpathfinder.github.io/files/paper2.pdf

CVDF DYNAMIC—A Dynamic Fuzzy Testing Sample Generation Framework Based on BI-LSTM and Genetic Algorithm

Published in Sensors, 2022

TLDR: This paper proposes CVDF DYNAMIC, a fuzzy testing sample generation framework which utilizes genetic algorithm and BI-LSTM neural network. Experimental results demonstrate that CVDF DYNAMIC can generate dynamic samples with high quality and strong path depth detection ability.

Recommended citation: Ma M, Han L, Qian Y. CVDF DYNAMIC—A Dynamic Fuzzy Testing Sample Generation Framework Based on BI-LSTM and Genetic Algorithm[J]. Sensors, 2022, 22(3): 1265. http://jkpathfinder.github.io/files/paper1.pdf