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6th IEEE International Conference on Data Science in Cyberspace, DSC 2021

作者： Zuo, Jinxin Guo, Ziyu Gan, Jiefu Lu, Yueming School Of Cyberspace Security Beijing University Of Posts And Telecommunications Key Lab Of Trustworthy Distributed Computing And Service Ministry Of Education Beijing China School Of Information And Communication Engineering Beijing University Of Posts And Telecommunications Key Lab Of Trustworthy Distributed Computing And Service Ministry Of Education Beijing China China Cyber Security Review Technology And Certification Center Beijing China

ISBN: (纸本)9781665418157

Cyber resilience has become a strategic point of information security in recent years. In the face of complex attack means and severe internal and external threats, it is difficult to achieve 100% protection against information systems. It is necessary to enhance the continuous service of information systems based on network resiliency and take appropriate compensation measures in case of protection failure, to ensure that the mission can still be achieved under attack. This paper combs the definition, cycle, and state of cyber resilience, and interprets the cyber resiliency engineering framework, to better understand cyber resilience. In addition, we also discuss the evolution of security architecture and analyze the impact of cyber resiliency on security architecture. Finally, the strategies and schemes of enhancing cyber resilience represented by zero trust and endogenous security are discussed. © 2021 IEEE.

关键词： Information systems

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DSANE:A Dual Structure-Aware Network Embedding Approach for User Identity Linkage

DSANE:A Dual Structure-Aware Network Embedding Approach for ...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Zhihao Xiong Xiaqing Xie Xu Wu Yuan Peng Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education BUPT China school of cyberspace security BUPT China Libarary of BUPT China

Increasing popularity and diversity of social media networks has encouraged people to participate on multiple online social networks. Thus, linking account across different networks belonging to the same person becomes an emerging task and is important to many downstream tasks. The mainstream approach is to regard users as nodes in networks and embed them in a low-dimensional latent space, making it convenient to input into subsequent predictive models. Existing methods are still faced up with challenges of incompleteness of networks and insufficiency performance. We propose a novel method called DSANE-Dual Structure-Aware Network Embedding for user identity linkage based on deep neural network, which firstly optimizes networks by a complete-filter method, and then embeds both local and global information into nodes’ vectors, thus greatly enriches nodes’ representation. Experiments on real-world datasets show that our method greatly outperforms baselines.

关键词： Couplings Deep learning Social networking (online) Neural networks Diversity reception Predictive models Feature extraction

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Unveiling the secrets without data: can graph neural networks be exploited through data-free model extraction attacks? 24

Unveiling the secrets without data: can graph neural network...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yuanxin Zhuang Chuan Shi Mengmei Zhang Jinghui Chen Lingjuan Lyu Pan Zhou Lichao Sun Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education The Pennsylvania State University SONY AI Huazhong University of Science and Technology Lehigh University

ISBN: (纸本)9781939133441

Graph neural networks (GNNs) play a crucial role in various graph applications, such as social science, biology, and molecular chemistry. Despite their popularity, GNNs are still vulnerable to intellectual property threats. Previous studies have demonstrated the susceptibility of GNN models to model extraction attacks, where attackers steal the functionality of GNNs by sending queries and obtaining model responses. However, existing model extraction attacks often assume that the attacker has access to specific information about the victim model's training data, including node attributes, connections, and the shadow dataset. This assumption is impractical in real-world scenarios. To address this issue, we propose STEAL-GNN, the first data-free model extraction attack framework against GNNs. STEALGNN advances prior GNN extraction attacks in three key aspects: 1) It is completely data-free, as it does not require actual node features or graph structures to extract GNN models. 2) It constitutes a full-rank attack that can be applied to node classification and link prediction tasks, posing significant intellectual property threats across a wide range of graph applications. 3) It can handle the most challenging hard-label attack setting, where the attacker possesses no knowledge about the target GNN model and can only obtain predicted labels through querying the victim model. Our experimental results on four benchmark graph datasets demonstrate the effectiveness of STEALGNN in attacking representative GNN models.

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Grammatical Error Correction via Mixed-Grained Weighted Training

arXiv

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arXiv 2023年

作者： Li, Jiahao Wang, Quan Zhu, Chiwei Mao, Zhendong Zhang, Yongdong University of Science and Technology of China Hefei China MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

The task of Grammatical Error Correction (GEC) aims to automatically correct grammatical errors in natural texts. Almost all previous works treat annotated training data equally, but inherent discrepancies in data are neglected. In this paper, the inherent discrepancies are manifested in two aspects, namely, accuracy of data annotation and diversity of potential annotations. To this end, we propose MainGEC, which designs token-level and sentence-level training weights based on inherent discrepancies in accuracy and potential diversity of data annotation, respectively, and then conducts mixed-grained weighted training to improve the training effect for GEC. Empirical evaluation shows that whether in the Seq2Seq or Seq2Edit manner, MainGEC achieves consistent and significant performance improvements on two benchmark datasets, demonstrating the effectiveness and superiority of the mixed-grained weighted training. Further ablation experiments verify the effectiveness of designed weights of both granularities in MainGEC. Copyright © 2023, The Authors. All rights reserved.

关键词： Error correction

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A&D Graph-Based Graph Neural Network Intrusion Detection for In-Vehicle Controller Area Network

A&D Graph-Based Graph Neural Network Intrusion Detection for...

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IEEE International Conference on Communications in China (ICCC)

作者： Yaru He Jiaqi Gao Mingrui Fan Daoqi Han Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The rapid advancement of the Internet of Vehicles (IoV) has significantly enhanced the diversity and intelligence of Electronic Control Units (ECUs) within the Controller Area Network (CAN) of vehicles. Implementing an effective Intrusion Detection System (IDS) is paramount for ensuring the security of the in-vehicle CAN. Traditional approaches in intrusion detection often rely on single-sample analysis, demanding extensive labeled data. To circumvent this limitation, a graph-based IDS that eliminates the necessity for single-sample labeling becomes imperative. Nonetheless, existing graph-based IDS models encounter the challenge of achieving high accuracy, especially for impersonation attacks. In this paper, we propose Arbitration&Data graph-based Graph Neural Networks (A&D-GNN) IDS to improve detection accuracy for in-vehicle CAN effectively. The proposed A&D-GNN IDS can significantly improve detection accuracy for impersonation attacks, which includes the Arbitration&Data (A&D) graph construction algorithm, representation learning, and intrusion detection. Based on the CAN message characteristics, the A&D directed attribute graph can extract comprehensive information, including information about both the arbitration field and the data field in the CAN message frame, notably, the data field is associated with the arbitration field. Therefore, the following Graph Neural Networks (GNN) with powerful representation capabilities can learn more precise graph-level representations. Lastly, a multi-classification layer is applied to perform intrusion detection. Experimental results on the public benchmark OTIDS dataset show that our proposed model is superior to other models with higher accuracy and an impressive accuracy of 99.92% for impersonation attacks.

关键词： Representation learning Accuracy Intrusion detection Impersonation attacks Data processing Graph neural networks Security

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MADRL Based Uplink Joint Resource Block Allocation and Power Control in Multi-Cell Systems

MADRL Based Uplink Joint Resource Block Allocation and Power...

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IEEE Conference on Wireless Communications and Networking

作者： Yuhan Yang Tiejun Lv Yingping Cui Pingmu Huang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

Intelligent resource allocation and power control schemes are regarded as important methods to alleviate the problems caused by the sharp increase in the number of users and operating costs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based algorithm to jointly optimize resource block (RB) allocation and power control, which aims to maximize the average spectrum efficiency (SE) of the system while meeting quality of service (QoS) constraints. In view of the fact that centralized training distributed execution retains the advantages of centralized training while reducing the amount of computation and signaling overhead, the MADRL technique can be adopted. In the proposed MADRL model, the Q function of each agent is aggregated through the value decomposition network, which strengthens the cooperation of agents and improves the convergence of the algorithm. We add a reward discount network into the original MADRL framework to adaptively adjust the attention to future rewards according to the performance of agents in the training process. Simulation experiments show that the proposed algorithm has better performance and stability than the existing alternatives.

关键词： Training Deep learning Power control Quality of service Reinforcement learning Stability analysis Resource management

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Real Time Attrition Prediction Mechanism Based on Deep Learning

Real Time Attrition Prediction Mechanism Based on Deep Learn...

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Signal Processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Chen Wu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

Modern companies mostly have their own employee management systems, which retain a large amount of employee behavior data. These data can be used to mine valuable information, such as whether employees have attrition intentions. This paper proposes a mechanism of attrition prediction based on deep learning, which mainly includes three modules: (1) Using LSTM to build a performance-attrition pre-trained model; (2)Using Transformer to build a sentiment tendency model based on employee weekly reports; (3) Based on (1), (2), establishing Real Time Attrition Prediction Mechanism in the company's employee management system to predict employees' willingness to leave at any time. The purpose of this mechanism is to help companies manage better and reduce losses caused by employee attrition.

关键词： Deep learning Companies Predictive models Signal processing Transformers Real-time systems Behavioral sciences

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Enhancing Algorithmic Fairness Through Counterfactual Data Augmentation

SSRN

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SSRN 2024年

作者： Xu, Yingchun Zhang, Xiaohang Xue, Haoyu Li, Zhengren School of Economics and Management Beijing University of Posts and Communications Beijing100876 China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China

In high-stakes AI fields such as criminal justice, loan applications, and graduate admissions, concerns about algorithmic discrimination are increasingly growing. This study proposes a novel counterfactual data augmentation method, termed Counterfactual Fairness Variational AutoEncoder (CFVAE), which integrates a Variational AutoEncoder and Causal model. The CFVAE is designed to enhance algorithmic fairness and transparency by generating both factual and counterfactual datasets. Moreover, the CFVAE framework introduces a novel counterfactual fairness loss function and utilizes cosine similarity to ensure the independence between exogenous variables and sensitive attributes, thereby guaranteeing the fairness quality of generated data. Extensive experiments have validated the effectiveness of our model, proving that it markedly improves the fairness of prediction models without substantial loss of data information while maintaining accuracy. © 2024, The Authors. All rights reserved.

关键词： Data accuracy

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MFTCN: A Multi-Frequency Temporal Convolutional Network for Enhancing Temperature Prediction Dependability

MFTCN: A Multi-Frequency Temporal Convolutional Network for ...

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

作者： Wei Huang Yuan Gao Xi angling Fu Zhiyi Song Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Huazhong University of Science and Technology Hubei China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Accurate temperature prediction plays a key role in various fields such as agriculture, ecology, and the environment. Traditional methods directly learn historical temperature changes to predict future temperature values. However, temperature changes are complex, and traditional methods are difficult to cope with nonlinear changes in temperature, resulting in poor temperature prediction accuracy. To address this issue, we attempt to decompose complex temperature changes into several simple components and propose a multi-frequency temporal convolutional network for short-term temperature prediction. Specifically, three key modules, namely multi - frequency decomposition, component prediction, and parameter optimization are employed to reduce the complexity of temperature changes and the uncertainty of prediction while ensuring that the original temperature information is not lost. On two real temperature datasets, our proposed method outperforms five baselines, which demonstrates that decomposing complex temperature changes is beneficial to improving temperature prediction accuracy.

关键词： Temperature distribution Temperature dependence Accuracy Uncertainty Biological system modeling Predictive models Data models Complexity theory Convolutional neural networks Optimization

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RBCP: A Reputation-based Blockchain Consensus Protocol for Internet of Things 24

RBCP: A Reputation-based Blockchain Consensus Protocol for I...

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shang, Jiaze Lu, Tianbo Cai, Yingjie Luo, Shuang Jin, Zhaoxin School of Computer Science National Pilot Software Enginerring School Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350319934

The application of blockchain in the Internet of Things (IoT) solves the problem of centralization, enables data interaction of IoT nodes in untrusted environments. However, most of the blockchain consensus protocols are computationally expensive and poorly scalable, which cannot meet the current demand for low power consumption and low latency of IoT devices. Meanwhile, IoT nodes suffer from many malicious attacks. An effective reputation evaluation scheme is valuable for establishing a trusted, secure IoT environment with certain applications. In this paper, we propose RBCP, a novel reputation-based blockchain consensus protocol, and apply it to the IoT. The protocol divides IoT nodes into multiple regions, and consensus nodes in each region are selected based on real-time updated reputation values, which are used to participate in light PoW consensus and finally propose blocks. Then, we model the deposits and profits of the nodes as Stackelberg game and analyze the relationship between them. To evaluate our proposed protocol, we compare it with existing consensus protocols through several experiments. Our experimental results show that RBCP has advantages in terms of TPS, consensus time, and communication consumption, proving that it has low latency and less consensus time under the premise of sufficient security. © 2022 IEEE.

关键词： Internet of things

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