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E-PRedictor: an approach for early prediction of pull request acceptance

Science China(Information Sciences) 2025年第5期68卷 380-395页

作者： Kexing CHEN Lingfeng BAO Xing HU Xin XIA Xiaohu YANG State Key Laboratory of Blockchain and Data Security Zhejiang University Software Engineering Application Technology Lab

A pull request(PR) is an event in Git where a contributor asks project maintainers to review code he/she wants to merge into a project. The PR mechanism greatly improves the efficiency of distributed software development in the opensource community. Nevertheless, the massive number of PRs in an open-source software(OSS) project increases the workload of developers. To reduce the burden on developers, many previous studies have investigated factors that affect the chance of PRs getting accepted and built prediction models based on these factors. However, most prediction models are built on the data after PRs are submitted for a while(e.g., comments on PRs), making them not useful in practice. Because integrators still need to spend a large amount of effort on inspecting PRs. In this study, we propose an approach named E-PRedictor(earlier PR predictor) to predict whether a PR will be merged when it is created. E-PRedictor combines three dimensions of manual statistic features(i.e., contributor profile, specific pull request, and project profile) and deep semantic features generated by BERT models based on the description and code changes of PRs. To evaluate the performance of E-PRedictor, we collect475192 PRs from 49 popular open-source projects on GitHub. The experiment results show that our proposed approach can effectively predict whether a PR will be merged or not. E-PRedictor outperforms the baseline models(e.g., Random Forest and VDCNN) built on manual features significantly. In terms of F1@Merge, F1@Reject, and AUC(area under the receiver operating characteristic curve), the performance of E-PRedictor is 90.1%, 60.5%, and 85.4%, respectively.

关键词： pull request prediction model GitHub

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Threshold Huffman-Based Covert Timing Channel Detection

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International Journal of Network security 2025年第1期27卷 61-71页

作者： Lin, Yuwei Chen, Yonghong Zhuang, Xiaolong Huang, Xuwen School of Computer Science and Technology Huaqiao University Fujian Xiamen361000 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Fujian Xiamen361000 China

Network covert timing channels in computer networks evade effective detection by traditional security mechanisms such as firewalls, posing a significant threat to network security. However, most existing methods struggle to detect such channels when the window size is small. Additionally, the emergence of the new ϵ-κlibur channel has resulted in a decline in performance for detection methods such as GAS, SnapCatch, and DNN when detecting this particular channel. In this paper, we investigate a Huffman tree-based approach for detecting anomalous nodes in traffic and achieving the classification of channels through the design of weight intervals. Our method achieves an average detection rate of over 95% for each channel. Additionally, compared to GAS, SnapCatch, and DNN, our method improves the detection rates for the ϵ-κlibur channel by 11.75%, 8.46%, and 4.81%, respectively. The experimental results demonstrate that our proposed approach successfully detects ϵ-κlibur and achieves detection in a small window. © (2025), (Femto Technique Co., Ltd.). All rights reserved.

关键词： Computer system firewalls

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Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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Tsinghua Science and technology 2024年第2期29卷 305-324页

作者： Yiqiao Cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain Technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of Computer Sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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LAPAA: A Secure and Lightweight Authentication Protocol for Edge Computing Built on ASCON Algorithm 6

LAPAA: A Secure and Lightweight Authentication Protocol for ...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Zhang, Linghui Zhou, Changli Wen, Jingliang HuaQiao University College of Computer Science and Technology Xiamen361021 China HuaQiao University Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China

ISBN: (纸本)9798350388374

Facing the challenges of next-generation network security, Mobile Edge Computing (MEC) serves as a decentralized model designed to meet the growing demands for data processing and reduce latency, making its security increasingly critical. However, with the rapid development of edge computing technology, traditional authentication protocols have become inadequate due to their complexity, insufficient protection of user anonymity, and neglect of resource constraints. These issues are at the core of current network security challenges. In response, this paper innovatively proposes LAPAA - a secure and lightweight authentication protocol for Edge computing built on ASCON algorithm. LAPAA effectively addresses the problems found in previous solutions and protects user privacy through anonymity by combining the symmetric encryption algorithm ASCON with asymmetric elliptic curve cryptography (ECC). It employs clever parameter and computation strategies to maintain key consistency between edge devices and MEC servers, while avoiding the leakage of users' real identities. Furthermore, due to the lightweight nature of the ASCON algorithm itself, LAPAA provides superior low-latency performance for the protocol. Additionally, through simulation verification and informal security analysis using the Scyther tool, LAPAA demonstrates its strong defense against various malicious threats and comparative evaluations with other protocols highlight LAPAA's significant advantages in various aspects. Collectively, this research offers a secure and streamlined authentication strategy for MEC contexts, underscoring LAPAA's inventive and groundbreaking approach to overcoming prevalent obstacles. © 2024 IEEE.

关键词： Authentication Protocol

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Association Pattern-aware Fusion for Biological Entity Relationship Prediction 38

Association Pattern-aware Fusion for Biological Entity Relat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jia, Lingxiang Ying, Yuchen Feng, Zunlei Zhong, Zipeng Yao, Shaolun Hu, Jiacong Duan, Mingjiang Wang, Xingen Song, Jie Song, Mingli State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co Ltd. China

Deep learning-based methods significantly advance the exploration of associations among triple-wise biological entities (e.g., drug-target protein-adverse reaction), thereby facilitating drug discovery and safeguarding human health. However, existing researches only focus on entity-centric information mapping and aggregation, neglecting the crucial role of potential association patterns among different entities. To address the above limitation, we propose a novel association pattern-aware fusion method for biological entity relationship prediction, which effectively integrates the related association pattern information into entity representation learning. Additionally, to enhance the missing information of the low-order message passing, we devise a bind-relation module that considers the strong bind of low-order entity associations. Extensive experiments conducted on three biological datasets quantitatively demonstrate that the proposed method achieves about 4%-23% hit@1 improvements compared with state-of-the-art baselines. Furthermore, the interpretability of association patterns is elucidated in detail, thus revealing the intrinsic biological mechanisms and promoting it to be deployed in real-world scenarios. Our data and code are available at https://***/hry98kki/PatternBERP. © 2024 Neural information processing systems foundation. All rights reserved.

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GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis 24

GROOT: Generating Robust Watermark for Diffusion-Model-Based...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Weizhi Li, Yue Lin, Dongdong Tian, Hui Li, Haizhou Huaqiao University Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen China

ISBN: (纸本)9798400706868

Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audiowatermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%. Our audio samples are available at https://***/. © 2024 ACM.

关键词： Audio watermarking

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A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective 38

A2PO: Towards Effective Offline Reinforcement Learning from ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qing, Yunpeng Liu, Shunyu Cong, Jingyuan Chen, Kaixuan Zhou, Yihe Song, Mingli College of Computer Science and Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the out-of-distribution problem. However, existing works often suffer from the constraint conflict issue when offline datasets are collected from multiple behavior policies, i.e., different behavior policies may exhibit inconsistent actions with distinct returns across the state space. To remedy this issue, recent advantage-weighted methods prioritize samples with high advantage values for agent training while inevitably ignoring the diversity of behavior policy. In this paper, we introduce a novel Advantage-Aware Policy Optimization (A2PO) method to explicitly construct advantage-aware policy constraints for offline learning under mixed-quality datasets. Specifically, A2PO employs a conditional variational auto-encoder to disentangle the action distributions of intertwined behavior policies by modeling the advantage values of all training data as conditional variables. Then the agent can follow such disentangled action distribution constraints to optimize the advantage-aware policy towards high advantage values. Extensive experiments conducted on both the single-quality and mixed-quality datasets of the D4RL benchmark demonstrate that A2PO yields results superior to the counterparts. Our code is available at https://***/Plankson/A2PO. © 2024 Neural information processing systems foundation. All rights reserved.

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Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting 38

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shang, Zongjiang Chen, Ling Wu, Binqing Cui, Dongliang State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://***/shangzongjiang/Ada-MSHyper. © 2024 Neural information processing systems foundation. All rights reserved.

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Detection of Network Time Covert Channels Based on Image Processing 23

Detection of Network Time Covert Channels Based on Image Pro...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Huang, Xuwen Chen, Yonghong Li, Zhiqiang Zhan, Teng School of Computer Science and Technology Huaqiao University Xiamen Fujian China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen Fujian China

ISBN: (纸本)9798400700705

Network covert timing channels (NCTCs) utilize Inter-Packet Delay (IPD) encoding to hide data. It can be used for spreading malware and data leakage, posing severe threats to network security. With the increasing risk, the research on NCTC detection has become an important and urgent task. However, the detection methods based on IPD statistics are only effective for few types of channels and require large IPD sampling samples. The recent ML-based detection methods have multiple limitations due to their coarse-grained feature extraction method. In this paper, we propose a multi-channel image transformation method for extracting IPD features and select the lightweight network MobileVit for detection. We encode IPD one-dimensional time-series data into Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence plot (RP) images and stack them into dual-channel and three-channel images. After image transformation, we compare mainstream image classification networks and a self-built CNN. Experimental results show that our feature extraction image classification is more effective than the existing IPD extraction image transformation method. The MobileVit network shows better detection performance and accuracy, requiring fewer IPD sampling samples for detection windows. © 2023 ACM.

关键词： Sampling

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