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International Joint Conference on Neural networks (IJCNN)

作者： Aimei Dong Ruixin Wang Xuening Zhang Jian Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The spread of COVID-19 has brought great disaster to the world, and automatic segmentation of the infected region can help doctors make a quick diagnosis and reduce workload. However, automatic segmentation of infected regions in computed tomography (CT) images still faces many challenges. The most central problem at the moment is that when capturing low-frequency signals and high-frequency signals, the use of Transformer blocks for capturing low-frequency signals may deteriorate the high-frequency signals (local texture information) to some extent. For this reason, in this paper, we propose a new network for automatic segmentation of COVID-19 lung infections from CT images, called NextSeg. NextSeg follows an encoder-decoder architecture, where the encoder part uses NCB and NTB convolutional attention modules to extract better features while reducing the deterioration of texture profile information. The decoder part uses an attention fusion module to learn the weight relationship between features and retain more important feature information. In addition to the difficulty of extracting the boundary details of the COVID-19 infected region as well as the loss of details the deeper the network is, we designed a new hybrid strategy. Traditional and state-of-the-art approaches to the COVID-19 segmentation domain are compared. Extensive experiments show that our proposed framework outperforms existing competitors in both qualitative and quantitative results.

关键词： COVID-19 Image segmentation Convolution Computed tomography Neural networks Lung Medical services

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Towards Black-Box Adversarial Attacks on Interpretable Deep Learning Systems

Towards Black-Box Adversarial Attacks on Interpretable Deep ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhan, Yike Zheng, Baolin Wang, Qian Mou, Ningping Guo, Binqing Li, Qi Shen, Chao Wang, Cong School of Cyber Science and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Hubei China Institute of Network Sciences and Cyberspace BNRist Tsinghua University Beijing China School of Cyber Science and Engineering Xi'an Jiaotong University Shaanxi China City University of Hong Kong Departrnent of Computer Science Hong Kong Hong Kong

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recent works have empirically shown that neural network interpretability is susceptible to malicious manipulations. However, existing attacks against Interpretable Deep Learning Systems (IDLSes) all focus on the white-box setting, which is obviously unpractical in real-world scenarios. In this paper, we make the first attempt to attack IDLSes in the decision-based black-box setting. We propose a new framework called Dual Black-box Adversarial Attack (DBAA) which can generate adversarial examples that are misclassified as the target class, yet have very similar interpretations to their benign cases. We conduct comprehensive experiments on different combinations of classifiers and interpreters to illustrate the effectiveness of DBAA. Empirical results show that in all the cases, DBAA achieves high attack success rates and Intersection over Union (IoU) scores. © 2022 IEEE.

关键词： Learning systems

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Burst Sequence Based Graph Neural network for Video Traffic Identification

Burst Sequence Based Graph Neural Network for Video Traffic...

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15th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2024

作者： Xiong, Jian Bao, YingShuo Li, ShengBao Zhao, Kun Peng, LiZhi Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Quancheng Laboratory Jinan China School of Cyber Security University of Chinese Academy of Sciences Beijing China Shandong Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Jinan China Inspur Computer Technology Co. Ltd. Jinan China

ISBN: (纸本)9783031893599

With the vigorous development of video applications and the comprehensive upgrade of encryption protocols, video content review is becoming increasingly difficult. As a practical solution, video traffic identification has attracted more and more attention in recent years. In order to achieve more refined video traffic recognition, a primary problem to be solved is that the extracted traffic features lack higher-level semantic information to reflect changes in video content. Fortunately, under the same network conditions, similar types of video clips or traffic data generated by live video often have the same traffic change pattern, which allows different types of video traffic to be distinguished. Therefore, this paper extracts burst sequences from video traffic to represent video changes and constructs a graph structure based on burst sequences to extract high-level semantic information. With the powerful performance of a graph neural network, we transform video traffic classification into a graph classification problem and introduce GraphBurst, a model based on burst sequences and graph neural networks. We collected real-world traffic data from different network environments with five video types, including on-demand live streaming, with more than one hundred thousand streams. We launch a set of comparing experiments on the dataset, and compared with the existing methods, the experimental results show that our method has higher accuracy and interpretability. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Graph neural networks

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Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV networking

Blockchain-Based Identity Authentication Oriented to Multi-C...

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IEEE International Conference on Blockchain (Blockchain)

作者： Zesong Dong Wei Tong Zhiwei Zhang Jian Li Weidong Yang Yulong Shen Hangzhou Institute of Technology Xidian University Hangzhou China Shaanxi Key Laboratory of Network and System Security Xi’an China Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi’an China School of Computer Science and Technology Xidian University Xi’an China

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption.

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A Robust key Exchange and Tamper-Resistant Protocol for HAN and NAN networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area networks (HAN) and Neighborhood Area networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Provably Secure Efficient key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV networking

arXiv

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arXiv 2023年

作者： Dong, Zesong Tong, Wei Zhang, Zhiwei Li, Jian Yang, Weidong Shen, Yulong The Hangzhou Institute of Technology Xidian University Hangzhou China The School of Computer Science and Technology Xidian University Xi’an China The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi’an China Shaanxi Key Laboratory of Network and System Security Xi’an China

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain-based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Spatio-temporal GNN-Based Cell-Free Massive MIMO network with Maximal Benefit-Cost Ratio

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IEEE Transactions on Vehicular technology 2025年

作者： Jiang, Jing Li, Yanni Ye, Yinghui Feng, Dan Zhang, Jiayi Sutthiphan, Worakrin Niyato, Dusit Xi'an Uni versity of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Beijing Jiaotong University Frontiers Science Center for Smart High-Speed Railway System Beijing100044 China National Telecommunication Public Company Mobile System Development Department Bangkok10210 Thailand Nanyang Technological University College of Computing and Data Science Singapore

Due to the network's rapid expansion, mobile networks bear extra high deployment costs. Being a key enabler of 6th generation (6G) mobile networks, it is crucial that cell-free massive MIMO (CF mMIMO) networks achieve green and cost-effective development. In this correspondence, we propose spatio-temporal graph neural networks (STGNN)-based CF mMIMO networks with a maximal benefit-cost ratio. First, we formulate a new coverage cost metric named benefit-cost ratio (BCR), which can evaluate the deployment cost while fulfilling various traffic requirements. Next, CF mMIMO networks are modeled as a spatio-temporal graph to utilize STGNN to extract the spatial-temporal properties of traffic demands. Finally, STGNN optimizes the number and location of access points (APs) based on the maximal BCR criterion so that the CF mMIMO networks can achieve on-demand service capability with minimum deployment cost. Numerical results demonstrate that the proposed algorithm can significantly reduce the deployment cost while providing on-demand and high-quality mobile service. © 1967-2012 IEEE.

关键词： Mobile telecommunication systems

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UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Communications Based on DRL 100

UAV-RIS-Aided Energy-Efficient and QoS-Aware Emergency Commu...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Hu, Jiawei Ju, Ying Wang, Haoyu Liu, Lei Pei, Qingqi Shee, Y.G. Zhu, Xiaojie Wu, Celimuge University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States Xidian University Guangzhou Institute of Technology Guangzhou510555 China Xi'an University of Posts Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Xi'an710121 China Universiti Tunku Abdul Rahman Department of Electrical and Electronic Engineering Kajang43000 Malaysia King Abdullah University of Science and Technology Department of Computer Electrical and Mathematical Sciences and Engineering Thuwal239556900 Saudi Arabia The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan

ISBN: (纸本)9798331517786

Ensuring reliable communication can be incredibly challenging in emergencies due to the breakdown of conventional infrastructure. However, a promising solution is on the horizon: the integration of reconfigurable intelligent surfaces (RIS) onto unmanned aerial vehicles (UAV), known as UAV-RIS. This innovative approach holds the potential to offer agile and adaptable communication services during crises, overcoming the limitations of traditional systems. This paper establishes an innovative UAV-RIS system with an active RIS to enhance the uplink communication between ground devices (GDs) and the air base station (ABS). We present an advanced communication strategy utilizing deep reinforcement learning (DRL) for UAV-RIS-supported uplink communication in dynamic emergencies. This scheme is designed to optimize the energy efficiency of the UAV-RIS communication system while adhering to quality of service (QoS) constraints for all GDs. It achieves this by jointly optimizing the trajectory of the UAV-RIS and the phase of the active RIS, ensuring efficient and reliable communication in challenging environments. To optimize the performance of the system, we propose a hierarchical Proximal Policy Optimization (H-PPO) algorithm and the upper and lower layers of H-PPO optimize the trajectory and phase control, respectively. Simulation results demonstrate that our scheme can effectively learn the communication strategy to enhance the performance of dynamic emergency communication networks. © 2024 IEEE.

关键词： active reconfigurable intelligent surface deep reinforcement learning quality of service UAV trajectory

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Channel Drop Out:A Simple Way to Prevent CNN from Overfitting in Motor Imagery Based BCI

Channel Drop Out:A Simple Way to Prevent CNN from Overfittin...

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2021国际计算机前沿大会

作者： Jing Luo Yaojie Wang Rong Xu Guangming Liu Xiaofan Wang Yijing Gong Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and EngineeringXi'an University of Technology Shaanxi Province Institute of Water Resources and Electric Power Investigation and Design

With the development of deep learning,many motor imagery brain-computer interfaces based on convolutional neural networks(CNNs) show outstanding ***,the trial number of EEG in the training set is usually limited,and redundancy extensively exists in multiple channel ***,overfitting often appears in CNN based motor imagery recognition model and greatly affects the performances of *** this paper,channel drop out is proposed to address this problem by data augmentation and ensemble ***,one of all EEG channels will be dropped and replaced by the mean signal of all EEG *** this way,the trial number in the training set was enlarged by channel drop *** at the testing stage,all the EEG trials processed by channel drop out were fed to the CNN model and the average output probabilities of them were applied to determine the *** experiments were conducted on two popular CNN models applied in motor imagery BCI and BCI Competition IV datasets 2 a to verify the performances of the proposed channel drop out *** results show that average improvements provided by channel drop out in two-category or four-category motor imagery classification are 2.83%and 2.65% compared with the original CNN *** the channel drop out approach significantly improves the performances of motor imagery based BCI.

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