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International Conference on computer Supported Cooperative Work in Design

作者： Changnan Jiang Chunhe Xia Mengyao Liu Chen Chen Huacheng Li Tianbo Wang Pengfei Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the traditional centralized Android malware classification framework, privacy concerns exist due to collected users’ apps containing sensitive information. A new classification framework based on Federated Learning (FL) has emerged to protect privacy. However, significant spatiotemporal heterogeneity exists in the distribution of Android malware samples in different clients. It presents a huge challenge to existing FL schemes, as trained local models differ significantly, resulting in slower model convergence and lower classification accuracy. To bridge this gap, we propose FedDRC, a robust FL-based Android malware classifier. First, we design a functional semantic embedding mechanism of API features, FSEM, using word embedding to improve the robustness of the model to the time heterogeneity of the client’s samples. Secondly, we use the idea of Information Bottleneck (IB) and transfer learning to design a robust local model, PAMIB, to deal with the model degradation caused by the space heterogeneity of the distribution of client samples. Extensive experiments on the Androzoo dataset show that FedDRC has the best robustness for Android malware classification tasks in various heterogeneity distribution settings: fastest convergence and best classification accuracy.

关键词： Degradation Privacy Accuracy Federated learning Transfer learning Semantics Malware

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FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protection Method for Federated Android Malware Classifier

FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Changnan Jiang Chunhe Xia Mengyao Liu Rui Fang Pengfei Li Huacheng Li Tianbo Wang Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The federated Android malware classifier has attracted much attention owing to its advantages of privacy protection and multi-party joint modeling. However, the research indicates that the gradient transmitted within the federated classifier still encodes the user's sensitive information, exposing it to indirect privacy inference threats from curious servers. Differential privacy is a recognized and effective way to address this privacy breach threat by adding noise to the user's model parameters to limit the attacker's inference of sensitive information. However, the protection effect of existing differential privacy methods is at the cost of significantly reducing the model's classification accuracy, and it cannot be reasonably balanced. To address this challenge, we propose a privacy protection method, FedDADP. FedDADP performs adaptive, lightweight privacy configuration in its training time dimension and model space dimension according to the privacy risk distribution law in the federated Android malware classifier to protect users' privacy while maintaining the model's utility. Numerous experiments on the Androzoo dataset and multiple baseline classifiers show that FedDADP protects users' sensitive information better (7% more effectiveness against adversaries' inference) than baseline differential privacy methods and achieves better model utility (classification accuracy improves by about 8%) with the same privacy budget.

关键词： Training Privacy Adaptation models Differential privacy Accuracy Privacy breach Malware

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Automated testing of Android Applications Integrating Residual Network and Deep Reinforcement Learning

Automated Testing of Android Applications Integrating Residu...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Lizhi Cai Jilong Wang Mingang Cheng Jin Wang School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing&Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9781665458146

With the improvements of Deep Reinforcement Learning (DRL), there have been tremendous interests in utilizing DRL for automated application testing. However, most automated testing methods based on reinforcement learning have the problem of interacting with invalid UI areas and invalid interactions with controls. To solve this problem, this paper extracts the page features, constructs the Interactive Control Feature Diagram(ICCD); improves the DDQN network structure, adds the residual network, makes the algorithm take the picture as the input, and splits the original single output action(n*w*h) into two successive outputs: the interaction(1,n) and the position(1,w*h); a new reward function which combines the interaction times and the image similarity of ICCD is proposed to explore different UIs and ensure that there will be more than one action will be executed under the same UI. Experiments are carried out on five open source applications. The experimental results show that the proposed method is superior to other methods in code coverage and branch coverage.

关键词： Adaptation models software algorithms Reinforcement learning software quality Feature extraction software reliability Security

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Reports Aggregation of Crowdsourcing Test Based on Feature Fusion

Reports Aggregation of Crowdsourcing Test Based on Feature F...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Lizhi Cai Naiqi Wang Mingang Chen Jin Wang Jilong Wang Jiayu Gong Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China School of Information Science and Engineering. East China University of Science and Technology Shanghai China

ISBN: (纸本)9781665478373

In recent years, a new testing method based on the concept of crowdsourcing has made great progress. Developers upload the project to the crowdsourcing test platform and recruit a large number of crowdsourcing workers for testing, so that the testing process has higher test adequacy, faster testing speed and lower testing cost. However, the test reports submitted after the test have serious problems such as large quantity and high similarity, resulting in the failure to achieve the expected results. Based on the method of feature fusion, this paper integrates the text description information, bug type information and screenshot information of crowdsourcing test reports, clusters crowdsourcing test reports through the calculation of similarity between reports, and finally achieves better results.

关键词： Crowdsourcing Costs Conferences Machine learning software quality Natural language processing Mathematical models

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Research progress in the processing of crowdsourced test reports 14

Research progress in the processing of crowdsourced test rep...

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14th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communications, TridentCom 2019

作者： Wang, Naiqi Cai, Lizhi Chen, Mingang Zhang, Chuwei School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Foreign Affairs Service Center Shanghai China

ISBN: (纸本)9783030432140

In recent years, crowdsourced testing, which uses collective intelligence to solve complex software testing tasks has gained widespread attention in academia and industry. However, due to a large number of workers participating in crowdsourced testing tasks, the submitted test reports set is too large, making it difficult for developers to review test reports. Therefore, how to effectively process and integrate crowdsourced test reports is always a significant challenge in the crowdsourced testing process. This paper deals with the crowdsourced test reports processing, sorts out some achievements in this field in recent years, and classifies, summarizes, and compares existing research results from four directions: duplicated reports detection, test reports aggregation and classification, priority ranking, and reports summarization. Finally explored the possible research directions, opportunities and challenges of the crowdsourced test reports. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering. 2020.

关键词： software testing

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FLSSM: A Federated Learning Storage Security Model with Homomorphic Encryption

arXiv

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arXiv 2025年

作者： Li, Yang Xia, Chunhe Li, Chang Li, Xiaojian Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing100191 China Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China Guangxi Collaborative Innovation Center Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China College of Computer Science and Information Technology Guangxi Normal University Guilin541004 China School of Cyber Science and Technology Beihang University Beijing100191 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

Federated learning based on homomorphic encryption has received widespread attention due to its high security and enhanced protection of user data privacy. However, the characteristics of encrypted computation lead to three challenging problems: "computation-efficiency", "attack-tracing" and "contribution-assessment". The first refers to the efficiency of encrypted computation during model aggregation, the second refers to tracing malicious attacks in an encrypted state, and the third refers to the fairness of contribution assessment for local models after encryption. This paper proposes a federated learning storage security model with homomorphic encryption (FLSSM) to protect federated learning model privacy and address the three issues mentioned above. First, we utilize different nodes to aggregate local models in parallel, thereby improving encrypted models’ aggregation efficiency. Second, we introduce trusted supervise nodes to examine local models when the global model is attacked, enabling the tracing of malicious attacks under homomorphic encryption. Finally, we fairly reward local training nodes with encrypted local models based on trusted training time. Experiments on multiple real-world datasets show that our model significantly outperforms baseline models in terms of both efficiency and security metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model

arXiv

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arXiv 2023年

作者： Li, Yang Xia, Chunhe Li, Chang Wang, Tianbo The Key Laboratory of Beijing Network Technology Beihang University Beijing100191 China The Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin541004 China The School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou450000 China The School of Cyber Science and Technology Beihang University Beijing100191 China The Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

With the increasing importance of machine learning, the privacy and security of training data have become critical. Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant attention for addressing this concern. However, a challenge arises in federated learning due to the Byzantine Attack Problem, where malicious local models can compromise the global model's performance during aggregation. This article proposes the Blockchainbased Byzantine-Robust Federated Learning (BRLF) model that combines federated learning with blockchain technology. This integration enables traceability of malicious models and provides incentives for locally trained clients. Our approach involves selecting the aggregation node based on Pearson's correlation coefficient, and we perform spectral clustering and calculate the average gradient within each cluster, validating its accuracy using local dataset of the aggregation nodes. Experimental results on public datasets demonstrate the superior byzantine robustness of our secure aggregation algorithm compared to other baseline byzantine robust aggregation methods, and proved our proposed model effectiveness in addressing the resource consumption problem. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive Information Leakage in Federated Learning-based Android Malware Classifier

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensiti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning Information leakage Malware Security Risk management

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HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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Chemical reaction-inspired dual-population co-evolutionary algorithm for many-objective optimization

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Expert Systems with Applications 2025年 289卷

作者： Weichao Ding Mingshan Chen Wenbo Dong Fei Luo Chunhua Gu School of Information Science and Engineering East China University of Science and Technology 130 Meilong Road Shanghai 200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

Many-objective optimization problems (MaOPs) refer to the multi-objective problems with more than three conflicting objectives that need to be optimized simultaneously, which are prevalent but more challenging in scientific research and engineering applications. Existing many-objective optimization algorithms mainly make improvements in environmental selection strategies but rarely focus on the generation of offspring, and balancing convergence and diversity is still very difficult. To address these issues, this paper employs four basic collision operators in chemical reaction optimization to generate offspring and proposes a novel chemical reaction-inspired dual-population co-evolutionary algorithm (called DPCRO), which aims to further balance convergence and diversity to improve the quality of solutions in solving MaOPs. The proposed DPCRO consists of two independent and parallel populations, which are the uni-molecular evolutionary population (UE) and the bi-molecular evolutionary population (BE). UE adopts the uni-molecular collision operators for individual convergence optimization and provides convergence information for BE, while BE uses the bi-molecular collision operators for individual diversity optimization and provides diversity information for UE. These two populations work collaboratively to effectively balance their own convergence and diversity through collaborative interaction mechanisms. We conduct performance comparison experiments using multiple benchmark test problems with different characteristics. The experimental results show that the proposed algorithm not only enhances the performance of solving low-dimensional multi-objective problems, but also has strong competitiveness in addressing MaOPs.

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