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PPBFL: A Privacy Protected Blockchain-based Federated Learning Model

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arXiv 2024年

作者： Li, Yang Xia, Chunhe Lin, Wanshuang 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 Cyber Science and Technology Beihang University Beijing100191 China The Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China

With the rapid development of machine learning and a growing concern for data privacy, federated learning has become a focal point of attention. However, attacks on model parameters and a lack of incentive mechanisms hinder the effectiveness of federated learning. Therefore, we propose A Privacy Protected Blockchain-based Federated Learning Model (PPBFL) to enhance the security of federated learning and encourage active participation of nodes in model training. Blockchain technology ensures the integrity of model parameters stored in the InterPlanetary File System (IPFS), providing protection against tampering. Within the blockchain, we introduce a Proof of Training Work (PoTW) consensus algorithm tailored for federated learning, aiming to incentive training nodes. This algorithm rewards nodes with greater computational power, promoting increased participation and effort in the federated learning process. A novel adaptive differential privacy algorithm is simultaneously applied to local and global models. This safeguards the privacy of local data at training clients, preventing malicious nodes from launching inference attacks. Additionally, it enhances the security of the global model, preventing potential security degradation resulting from the combination of numerous local models. The possibility of security degradation is derived from the composition theorem. By introducing reverse noise in the global model, a zero-bias estimate of differential privacy noise between local and global models is achieved. Furthermore, we propose a new mix transactions mechanism utilizing ring signature technology to better protect the identity privacy of local training clients. Security analysis and experimental results demonstrate that PPBFL, compared to baseline methods, not only exhibits superior model performance but also achieves higher security. Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

An Identification Method of Image-based for Output Pattern of Metamorphic Relation in Burnup Calculation Program

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An Identification Method of Image-based for Output Pattern o...

Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Shengfu Fu Xiaohua Yang Meng Li Fan Wang Engineering and Technology Research Center of Software Evaluation and Testing for Intellectual Equipment of Hunan Province Hengyang of Hunan Prov. China CNNC Key Laboratory on High Trusted Computing Hengyang of Hunan Prov. China School of Computer Science and Technology University of South China Hengyang China

ISBN: (纸本)9781665402682

It is challenging to verify the design and analysis software adequately for nuclear power areas because of the inflated cost, time-consuming benchmark problems development, and few verification examples. This situation is called the test oracle problem. Metamorphic testing cleverly combines the specific properties of physical equations and numerical algorithms with software verification. Without constructing numerical solutions or verification examples, it verifies the software by checking whether the input and output of multiple executions meet the metamorphic relations. Since metamorphic relation is the nature rules of physical models and numerical algorithms, it is not restricted to specific code implementation technology. Therefore, it is no doubt that metamorphic testing is a promising technique to alleviate the test oracle *** relation is the key to metamorphic testing, which consists of input pattern and output pattern. The former can be obtained through manual analysis by industry experts, while the latter does not have a system identification method. An output pattern identification method based on the solution figure has been introduced here. Specifically, the input pattern is received by artificially analyzing the physical property of the program under test. Next, a group of test inputs has been generated according to such patterns. The calculation results are received after those inputs drive the program. Moreover, the function form of the output pattern is guided by the image of the results. Finally, metamorphic relation is constructed by input pattern and output pattern. For a demonstration of this method’s details, a nuclide burnup calculation code is employed here, namely NUIT. By investigating the resulting image of 3820 isotopes, their output pattern classification models have been established according to the burnup depth and nuclide density. It includes four categories and seventeen sub-categories. The verification of other burnup

关键词： Technological innovation Codes software algorithms Pattern classification Manuals software Mathematical models

EFwork: An Efficient Framework for Constructing a Malware Knowledge Graph

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EFwork: An Efficient Framework for Constructing a Malware Kn...

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

作者： Chen Chen Chunhe Xia Tianbo Wang Wanshuang Lin Yuan Zhao Yang 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

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Malware Knowledge Graph (MKG) serves as an essential auxiliary tool for malware detection and analysis. However, the construction of MKG faces several challenges, such as inadequate dataset quality, incomplete entity feature extraction, and the limitations imposed by deep learning techniques. To address these issues, we present an Efficient Framework for constructing a malware knowledge graph (EFwork). Firstly, we build a High-Quality Dataset (HQDataset) and introduce a metric for data quality assessment based on knowledge coverage, timeliness, and density. Subsequently, we develop a Named Entity Recognition (NER) model that extracts character features, part-of-speech features, and word features from the data, leveraging deep learning models to identify malware-related entities. Finally, we implement a rule-based filtering mechanism, utilizing a comprehensive Rule Database to eliminate entities that do not conform to predefined rules. Experimental result shows that our HQDataset demonstrates superior data quality when compared to other open-source datasets. Furthermore, our NER model combined with our Rule Database outperforms existing models, achieving improvements of 0.67%, 0.74%, and 0.69% in Precision, Recall, and F1-Score, respectively.

关键词： Measurement Deep learning Databases Filtering Data integrity Knowledge graphs Feature extraction

REDA: Malicious Traffic Detection Based on Record Length and Frequency Domain Analysis

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REDA: Malicious Traffic Detection Based on Record Length and...

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

作者： Wanshuang Lin Chunhe Xia Tianbo Wang Chen Chen Yuan Zhao Weidong Zhou School of Cyber Science and Technology Beihang University Beijing China 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 Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The TLS encryption protocol plays a vital role in securing data transmission, but it also presents challenges for payload-based Network Intrusion Detection Systems (NIDS). Existing methods utilize statistical characteristics of side-channel features, such as the mean packet length, to identify encrypted traffic. However, packet lengths are constrained by the Maximum Segment Size (MSS) of the TCP protocol. This constraint causes the length-varied sequence to be encapsulated into segments of equal length, resulting in information loss. Moreover, flow-level statistical features are vulnerable to interference from noisy packets, making it challenging to detect malicious traffic injected with benign packets effectively. In this paper, we propose an encrypted traffic detection model based on Record length and frEquency Domain Analysis (REDA). First, we reconstruct the TLS Record Length Sequence (TRLS), which is a length-varied sequence, to capture differences in traffic content during transmission. Second, we employ the Discrete Fourier Transform (DFT) to extract frequency domain features of the TRLS, which are resistant to attacker interference. Finally, an improved One-Class Support Vector Machine (OCSVM) algorithm is devised for the unsupervised detection of malicious traffic, enabling the identification of unknown attacks. Experiments show that REDA is superior to other state-of-the-art methods in terms of accuracy by 2.44%.

关键词： Support vector machines Resistance Analytical models Privacy Protocols Frequency-domain analysis Computational modeling

FedForgery: Generalized Face Forgery Detection with Residual Federated Learning

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arXiv 2022年

作者： Liu, Decheng Dang, Zhan Peng, Chunlei Zheng, Yu Li, Shuang Wang, Nannan Gao, Xinbo The State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China The State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Shaanxi Xi’an710071 China The Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

With the continuous development of deep learning in the field of image generation models, a large number of vivid forged faces have been generated and spread on the Internet. These high-authenticity artifacts could grow into a threat to society security. Existing face forgery detection methods directly utilize the obtained public shared or centralized data for training but ignore the personal privacy and security issues when personal data couldn’t be centralizedly shared in real-world scenarios. Additionally, different distributions caused by diverse artifact types would further bring adverse influences on the forgery detection task. To solve the mentioned problems, the paper proposes a novel generalized residual Federated learning for face Forgery detection (FedForgery). The designed variational autoencoder aims to learn robust discriminative residual feature maps to detect forgery faces (with diverse or even unknown artifact types). Furthermore, the general federated learning strategy is introduced to construct distributed detection model trained collaboratively with multiple local decentralized devices, which could further boost the representation generalization. Experiments conducted on publicly available face forgery detection datasets prove the superior performance of the proposed FedForgery. The designed novel generalized face forgery detection protocols and source code would be publicly available at https://***/GANG370/FedForgery. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

PIG: Prompt Images Guidance for Night-Time Scene Parsing

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arXiv 2024年

作者： Xie, Zhifeng Qiu, Rui Wang, Sen Tan, Xin Xie, Yuan Ma, Lizhuang Department of Film and Television Engineering Shanghai University Shanghai200072 China Shanghai Key Laboratory of Computer Software Testing and Evaluating China Hanghai Engineering Research Center of Motion Picture Special Effects Shanghai200072 China School of Computer Science and Technology China Normal University Shanghai200062 China Chongqing Institute of East China Normal University Chongqing401120 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Night-time scene parsing aims to extract pixel-level semantic information in night images, aiding downstream tasks in understanding scene object distribution. Due to limited labeled night image datasets, unsupervised domain adaptation (UDA) has become the predominant method for studying night scenes. UDA typically relies on paired day-night image pairs to guide adaptation, but this approach hampers dataset construction and restricts generalization across night scenes in different datasets. Moreover, UDA, focusing on network architecture and training strategies, faces difficulties in handling classes with few domain similarities. In this paper, we leverage Prompt Images Guidance (PIG) to enhance UDA with supplementary night knowledge. We propose a Night-Focused Network (NFNet) to learn night-specific features from both target domain images and prompt images. To generate high-quality pseudo-labels, we propose Pseudo-label Fusion via Domain Similarity Guidance (FDSG). Classes with fewer domain similarities are predicted by NFNet, which excels in parsing night features, while classes with more domain similarities are predicted by UDA, which has rich labeled semantics. Additionally, we propose two data augmentation strategies: the Prompt Mixture Strategy (PMS) and the Alternate Mask Strategy (AMS), aimed at mitigating the overfitting of the NFNet to a few prompt images. We conduct extensive experiments on four night-time datasets: NightCity, NightCity+, Dark Zurich, and ACDC. The results indicate that utilizing PIG can enhance the parsing accuracy of UDA. The code is available at https://***/qiurui4shu/PIG. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

Research and Practice on Performance Test of Call Center Platform System

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Wei Song Chunye Du Chunliu Zhang Shanghai Key Laboratory of Computer Software Evaluating & Testing 1588 Lianhang Road Shanghai China

Call center is more and more closely linked with people's daily life. This paper analyzes the three major problems of call center service (cask effect, avalanche effect, cumulative effect), introduces the call center platform performance testing strategy, and uses the Sprint and SilkPerformer testing tools to test the call center platform. The experimental results show that, through the implementation of performance testing, can predict the processing ability of the call center system in real environment, the system will advance the problems exposed, positioning help problem solution.

关键词：

Message from program chairs

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Proceedings - 2018 IEEE/ACIS 19th International Conference o...

Proceedings - 2018 IEEE/ACIS 19th International Conference on software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018 2018年 ix页

作者： Yeo, Hyun Hong, Chung Sun Yeom, Gun Huck Cai, Lizhi Matsuo, Tokuro Fukuta, Naoki Hwang, Ha Jin SunChon National University Korea Republic of Kyung Hee University Korea Republic of Pusan National University Korea Republic of Shanghai Key Laboratory of Computer Software Evaluating and Testing China Advanced Institute of Industrial Technology Japan Shizuoka University Japan Sunway University Malaysia

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

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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