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Lingwei Chen

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4 papers

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AAAI Conference 2025 Short Paper

2-in-1 Phishing Detection via Large LM Distillation and Small LM Perturbation (Student Abstract)

Calvin Greenewald
Bradley Ashmore
Chien-Sing Poon
Lingwei Chen

Phishing emails are an escalating threat, underscoring the need for precise detection methods. While large language models (LLMs) have gained attention for their potential in this area, their reliance on extensive data for fine-tuning poses practical challenges. This paper introduces DualLM for phishing detection with minimal data, which distills the reasoning ability from a large LM to enhance a small target LM and integrates trainable perturbations to improve the small LM's inference capabilities. Experiments demonstrate that DualLM can benefit from dual LMs, which reduces training parameters and data required, while maintaining high performance in phishing email detection with limited data.

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AAAI Conference 2025 Conference Paper

AutoFEA: Enhancing AI Copilot by Integrating Finite Element Analysis Using Large Language Models with Graph Neural Networks

Shifu Hou
Rick Johnson
Ramandeep Makhija
Lingwei Chen
Yanfang Ye

Large Language Models (LLMs) have demonstrated significant potential across various applications, but their use as AI copilots in complex and specialized tasks is often hindered by AI hallucinations, where models generate outputs that seem plausible but are incorrect. To address this challenge, we develop AutoFEA, an intelligent system that integrates LLMs with Finite Element Analysis (FEA) to automate the generation of FEA input files. Our approach features a novel planning method and a graph convolutional network (GCN)-Transformer Link Prediction retrieval model, which enhances the accuracy and reliability of the generated simulations. The AutoFEA system proceeds with key steps: dataset preparation, step-by-step planning, GCN-Transformer Link Prediction retrieval, LLM-driven code generation, and simulation using CalculiX. In this workflow, the GCN-Transformer model predicts and retrieves relevant example codes based on relationships between different steps in the FEA process, guiding the LLM in generating accurate simulation codes. We validate AutoFEA using a specialized dataset of 512 meticulously prepared FEA projects, which provides a robust foundation for training and evaluation. Our results demonstrate that AutoFEA significantly reduces AI hallucinations by grounding LLM outputs in physically accurate simulation data, thereby improving the success rate and accuracy of FEA simulations and paving the way for future advancements in AI-assisted engineering tasks.

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IJCAI Conference 2019 Conference Paper

iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow

Yujie Fan
Yiming Zhang
Shifu Hou
Lingwei Chen
Yanfang Ye
Chuan Shi
Liang Zhao
Shouhuai Xu

As modern social coding platforms such as GitHub and Stack Overflow become increasingly popular, their potential security risks increase as well (e. g. , risky or malicious codes could be easily embedded and distributed). To enhance the social coding security, in this paper, we propose to automate cross-platform user identification between GitHub and Stack Overflow to combat the attackers who attempt to poison the modern software programming ecosystem. To solve this problem, an important insight brought by this work is to leverage social coding properties in addition to user attributes for cross-platform user identification. To depict users in GitHub and Stack Overflow (attached with attributed information), projects, questions and answers as well as the rich semantic relations among them, we first introduce an attributed heterogeneous information network (AHIN) for modeling. Then, we propose a novel AHIN representation learning model AHIN2Vec to efficiently learn node (i. e. , user) representations in AHIN for cross-platform user identification. Comprehensive experiments on the data collections from GitHub and Stack Overflow are conducted to validate the effectiveness of our developed system iDev integrating our proposed method in cross-platform user identification by comparisons with other baselines.

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IJCAI Conference 2019 Conference Paper

Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection

Yanfang Ye
Shifu Hou
Lingwei Chen
Jingwei Lei
Wenqiang Wan
Jiabin Wang
Qi Xiong
Fudong Shao

The increasingly sophisticated Android malware calls for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i. e. , app, API, device, signature, affiliation) and rich relations among them, we present a structured heterogeneous graph (HG) for modeling. To efficiently classify nodes (e. g. , apps) in the constructed HG, we propose the HG-Learning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HG embeddings at the first attempt. We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection. Comprehensive experiments on large-scale and real sample collections from Tencent Security Lab are performed to compare various baselines. Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.

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