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

作者： Beibei Feng Qi Wang Xiaolin Xu Yijing Wang Tianning Zang Jingrun Ma Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China National Computer Network Emergency Response Technical Team Coordination Center of China China Zhongguancun Laboratory China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The widespread adoption of DNS over HTTPS (DoH) has introduced significant challenges in network security, particularly the emergence of DoH tunnels. Existing methods, reliant on large labeled datasets, struggle to adapt to novel DoH tunnel variants. We present ProtoDoH, a novel framework for DoH tunnel detection that uniquely integrates prototypical networks with meta-learning. Our method leverages few-shot learning principles to detect DoH tunnels with minimal training samples, addressing the challenge of sample scarcity in new attack scenarios. By employing a metric-based meta-learning framework, ProtoDoH enables rapid adaptation to novel DoH tunnel variants, significantly reducing the detection time for new DoH tunnels. Experimental results demonstrate that our approach achieves over 99% accuracy in detecting DoH tunnels with as few as 5 samples, notably outperforming traditional machine learning and deep learning methods. Furthermore, our model exhibits strong generalization capabilities across different network environments and DoH tunnel tools.

关键词： Metalearning Training Measurement Privacy Accuracy Training data network security HTTP Few shot learning Resilience

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A Quantum Temporal Difference Learning Method Based on Quantum World Model

A Quantum Temporal Difference Learning Method Based on Quant...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Jianbo Du College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’an China School of Information Technology Carleton University Ottawa Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Based on quantum parallelism theory and quantum phenomena such as superposition and entanglement, quantum reinforcement learning (QRL) has the potential to surpass classical reinforcement learning (RL). Although some excellent works have been done on QRL, existing RL methods encounter chanllenges when performing in environments with sparse rewards. In this paper, we provide a new perspective on conducting temporal difference (TD) learning in quantum computing, which can eliminate redundant exploration steps compared to classical methods. Specifically, we first use environment information to construct a world model with quantum circuits, enabling it to interact in a quantum way. Then, we perform the learning process by using the quantum world model and Grover’s algorithm to query backwards how to reach the recorded states with high TD-errors. Simulation results show that our proposed method has superior performance compared to classical RL algorithms.

关键词： Quantum entanglement Computational modeling Simulation Temporal difference learning Parallel processing Global communication Integrated circuit modeling Quantum circuit

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Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

Map2Schedule: An End-to-End Link Scheduling Method for Urban...

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IEEE International Conference on Communications (ICC)

作者： Lihao Zhang Haijian Sun Jin Sun Ramviyas Parasuraman Yinghui Ye Rose Qingyang Hu School of Electrical and Computer Engineering University of Georgia Athens GA USA School of Computing University of Georgia Athens GA USA Shanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China Electrical and Computer Engineering Department at Utah State University Logan UT USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

关键词： Wireless communication Accuracy Processor scheduling Computational modeling System performance Urban areas Vehicular ad hoc networks

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SML: A Backdoor Defense for Non-Intrusive Speech Quality Assessment via Semi-Supervised and Multi-Task Learning

SML: A Backdoor Defense for Non-Intrusive Speech Quality Ass...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ying Ren Wenjie Zhang Jiahong Ye Jie Li Diqun Yan Bin Ma Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Digital Technology and Engineering Ningbo University of Finance and Economics Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Non-intrusive speech quality assessment (NISQA) is widely used in speech downstream tasks due to its ability to predict the quality of speech without a reference speech. However, few researchers have focused on the backdoor security of NISQA. Despite the backdoor defenses have been extensively studied to mitigate the threat of maliciously modifications in deep neural networks. In particular, semi-supervised based backdoor defenses have excellent defensive performance by depriving backdoor attacks of their most essential need. But these defense methods rely on data-augmentation consistency and thus cannot be applied to NISQA. In this work, we propose a backdoor defense based on semi-supervised and multi-task learning (SML). Semi-supervised learning is based on the simple assumption that the same input should be as consistent as possible in two similar models. Multi-task learning further improves the prediction performance of mean opinion score (MOS) by learning the tasks of perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI) and speech distortion index (SDI). Extensive experiments involving five backdoor defenses against five backdoor attacks on two benchmark datasets demonstrate the superiority of our SML approach.

关键词： Acoustic distortion Artificial neural networks Semisupervised learning Benchmark testing Multitasking Acoustics Quality assessment security Indexes Speech processing

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Fault Diagnosis for Rolling Bearing Based on RF-LSTM

Fault Diagnosis for Rolling Bearing Based on RF-LSTM

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2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021

作者： Li, Qiuting Wang, Xiuqing Yang, Yunpeng Wang, Ruiyi Lv, Feng Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China

ISBN: (纸本)9781665401159

In the era of big data, it is of great significance to study data-driven fault diagnostic methods for equipment in complex industrial environments. In this paper, a data-driven fault diagnostic method for rolling bearing based on RF-LSTM is proposed, which combines random forest (RF) algorithm with long and short term memory (LSTM) networks. RF is used to select sensor data's features, which have great influence on rolling bearing fault diagnosis, and LSTM is used for prediction rolling bearing's working states. The proposed method is used for fault diagnosis of the Case Western Reserve University (CWRU) bearing dataset and the experimental results validated the effectiveness of the proposed RF-LSTM model. Experimental results also show that the performance of the RFLSTM model is better than that of RF, LSTM, RF-RF, PCA-RF, PCA-LSTM models. © 2021 IEEE.

关键词： Failure analysis

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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A Novel Lightweight Dynamic Trust Evaluation Model for Edge Computing

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IEEE Transactions on network and Service Management 2025年

作者： Yang, Hao Guo, Liangmin Wang, Taochun Lv, Chengmei Anhui Technical College of Mechanical and Electrical Engineering School of Internet and Communication Wuhu241003 China Anhui Normal University School of Computer and Information Wuhu241003 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu241003 China

The temporal decay of trust data in dynamic edge computing environments leads to inaccurate evaluation, and the recommended trust values from heterogeneous nodes are affected by subjective bias and are vulnerable to malicious attacks. To address these issues, this paper proposes a novel lightweight dynamic trust evaluation model. First, a time decay function is derived based on Newton’s law of cooling to effectively reflect the impact of trust timeliness on trust evaluation. On this basis, the real value is iteratively calculated and used as the recommended trust value through the truth discovery algorithm, enhancing the accuracy of trust evaluation. Then, the weight of the recommended trust value in the comprehensive trust value is determined based on the standard deviation of the weight of perceived data from each recommending node, balancing the influence of subjective and objective factors on trust evaluation results. Lastly, the comprehensive trust value is dynamically weighted, and an incentive mechanism is employed to update trust data based on feedback, reflecting the dynamic nature of trust. Theoretical analysis demonstrates that the model presented in this paper exhibits low time and space complexities, meeting the lightweight requirements of the edge computing environment. Experimental results indicate high recommendation trust accuracy and interaction success rates, as well as effective resistance against malicious attacks. © 2004-2012 IEEE.

关键词： Trusted computing

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Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer

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Journal of Innovative Optical Health Sciences 2023年第4期16卷 120-133页

作者： Yuxin Li Qianlong Zhang Hang Zhou Junhuai Li Xiangning Li Anan Li Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Xi'an 710048P.R.China School of Computer Science Chengdu University of Information Technology Chengdu 610225P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P.R.China HUST-Suzhou Institute for Brainsmatics Suzhou 215123 P.R.China

Vascular segmentation is a crucial task in biomedical image processing,which is significant for analyzing and modeling vascular networks under physiological and pathological *** advances in fluorescent labeling and mesoscopic optical techniques,it has become possible to map the whole-mouse-brain vascular networks at capillary ***,segmenting vessels from mesoscopic optical images is a challenging *** problems,such as vascular signal discontinuities,vessel lumens,and background fluorescence signals in mesoscopic optical images,belong to global semantic information during vascular *** vascular segmentation methods based on convolutional neural networks(CNNs)have been limited by their insufficient receptive fields,making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation ***,we propose SegVesseler,a vascular segmentation method based on Swin *** adopts 3D Swin Transformer blocks to extract global contextual information in 3D *** approach is able to maintain the connectivity and topology of blood vessels during *** evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging *** experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

关键词： Vascular segmentation Swin Transformer mesoscopic optical imaging fMOST

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Qiuting Li Xiuqing Wang Wenxia Du College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China College of China Combustion Engineering Hebei Normal University Shijiazhuang China

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual network (ResNet), and Spiking Neural networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings.

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