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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Xiao Cui Xiaohui Han Guangqi Liu Wenbo Zuo Zhiwen Wang 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 Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Quancheng Provincial Laboratory Jinan China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

As an emerging machine learning method, Federated Learning is widely used in network anomaly detection scenarios. However, many current federated learning-based network anomaly detection works ignore the communication overhead problem during model training. The transmission of model parameters occupies a large amount of bandwidth, affecting the efficiency of network anomaly detection as well as the communication tasks of other programs. In this study, we propose eFedAD, an efficient federated learning framework for network traffic anomaly detection. Specifically, eFedAD uses singular value decomposition to compress the transmitted parameters and introduces a weighting mechanism to control the compression rate and aggregation proportion. In this way, eFedAD can significantly reduce the number of transmitted parameters and assign higher weights to more valuable clients, enhancing the generalization ability of the global model. Additionally, to address the slow convergence issue due to compressed parameters and minimize the total number of transmitted parameters, eFedAD uses a client selection method that is based on clustering clients’ data feature distributions. The experimental results demonstrate that eFedAD outperforms other compression methods and network anomaly detection approaches, achieving excellent performance.

关键词： Training Data privacy Federated learning Telecommunication traffic Data models Sensors Task analysis

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Response of the Yellow and East China seas low-trophic ecosystems to two typhoons at different translational speeds

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Journal of Oceanology and Limnology 2025年 1-21页

作者： Zhu, Lei Zhang, Jing Shi, Changcen Yang, Wei Zhang, Haiyan Wang, Yucheng Liu, Guangliang Bian, Changwei Zhao, Liang College of Marine and Environmental Science Tianjin University of Science and Technology Tianjin China Key Laboratory of Marine Resource Chemistry and Food Technology (TUST) Ministry of Education Tianjin China Key Laboratory of Ocean Observation and Information of Hainan Province Sanya China Tianjin Key Laboratory for Marine Environmental Research and Service School of Marine Science and Technology Tianjin University Tianjin China Laoshan Laboratory Qingdao China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory Ocean University of China Qingdao China

Frequent typhoons can significantly change the temperature, nutrient availability, and phytoplankton biomass in marginal seas. The oceanic response to typhoons is usually influenced by the features of the typhoon, among which the translational speed is critically important. By using a highresolution coupled physical-biological model, we investigated the response of the Yellow and East China seas (YECS) to two typhoons at different translational speeds, Muifa in August 2011 and Bolaven in August 2012. The model well reproduced the spatial and temporal variations of temperature, chlorophyll-a concentration over the YECS. Results show that typhoons with slower translational speeds uplift more deep water, leading to a more significant oceanic response. Divergence and convergence caused nutrient fluxes in opposite directions in the surface and bottom layers. Moreover, the nutrient flux in the bottom layer was greater than that in the surface layer. These phenomena are closely related to the spatial distribution of nutrients. Further studies show that the degree of ocean response to typhoons is highly correlated with the initial conditions of physical and biological elements of the upper ocean before the typhoon, as well as with ocean structure. Pretyphoon initial conditions of oceanic physical and ecological elements, mixed layer depth, and potential energy anomalies can all alter the degree of typhoon-induced oceanic response. This study emphasizes the important roles of the translational speed of typhoons and the initial oceanic conditions in the oceanic response to typhoons.

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Deep Video Cascade Clustering from Video Structures

SSRN

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SSRN 2024年

作者： Wang, Zihua Zhu, Hongyuan Mi, Siya Zhang, Yu Geng, Xin School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China A*STAR Singapore School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing211189 China

Video clustering is a critical unsupervised learning task where, unlike in supervised video classification, category labels are not *** main challenge lies in learning an effective video representation without annotations to group similar *** this paper, we introduce a novel unsupervised video learning paradigm called Deep Video Cascade Clustering ($\text{DVC}^2$).Unlike existing image-based clustering methods, $\text{DVC}^2$ first learns an initial video representation through frame clustering, which serves as guidance. Subsequently, the video clustering results are aligned with both the long-term and short-term video structures and their nearest *** conduct a comprehensive evaluation of $\text{DVC}^2$ on benchmark video datasets, including UCF101 and Kinetic-400, and achieve state-of-the-art video clustering results. Additionally, we evaluate the performance of combining self-supervised learning with K-means *** findings validate the effectiveness of $\text{DVC}^2$ in unsupervised video clustering scenarios. © 2024, The Authors. All rights reserved.

关键词： Unsupervised learning

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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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Research on point cloud registration of industrial parts based on FGR-ICP algorithm

Research on point cloud registration of industrial parts bas...

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2021 3rd International Conference on Applied Machine Learning and Data Science, ICAMLDS 2021

作者： Zha, Yongjian Chen, Ming Lu, Shenglian School of Computer and Information Engineering Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China

In the industrial application environment, there are some challenges to point cloud registration caused by disordered and occluded industrial parts. A point cloud registration method combining fast global alignment (FGR) and improved ICP is proposed for the problem that the traditional Iterative Closest Point (ICP) needs to rely on good initialization quality and easily falls into local optimal solutions. The method can optimize the different components of the objective function alternately by FGR to obtain the optimal initial transformation matrix. An improved ICP algorithm was used for exact matching of the initial transformation results. The experimental results showed that the method in this paper had greatly improved the matching accuracy, and the speed had been improved by an order of magnitude compared with the traditional method. © Published under licence by IOP Publishing Ltd.

关键词： Iterative methods

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Mobile Terminal security Protection Method Based on Community Detection Algorithms and Graph Matching networks 8th

Mobile Terminal Security Protection Method Based on Communit...

引用

8th International Conference on Artificial Intelligence and security , ICAIS 2022

作者： Chen, Mu Dai, Zaojian Li, Yong Zhang, Juling Wang, Sheng Zhou, Jian Wu, Fan Institute of Information and Communication Global Energy Interconnection Research Institute Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China Electric Power Research Institute of State Grid Sichuan Chengdu610041 China State Grid Sichuan Electric Power Company Chengdu610041 China Computer Science Department Tuskegee University TuskegeeAL36088 United States

ISBN: (纸本)9783031067631

The rapid growth in the number of malware apps poses a great danger to users of power mobile terminals. Among smart devices, devices using the Android system are very popular and thus are easy targets for malware attacks. In this paper, we propose a mobile terminal security protection method based on community detection algorithms and graph matching networks. First, the call relationships between functions are extracted from the decompiled APK files, and runtime information is collected as a supplement to complete the construction of the behavior graph. Second, based on the behavior graph of malware samples, we propose a method to extract the public behavior graph as the features of malware families. Finally, a graph matching network is used to learn and calculate the similarity between the public behavior graph and the software behavior graph, and use it as the basis for detection. Our evaluation of the dataset containing 1274 malware apps and 108 benign apps shows that the selected public behavior graph is effective for Android malware detection, with an overall accuracy of 90.4%. The experimental results show that the method has a high detection rate and a low false rate, and can detect malware and its variants. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mobile telecommunication systems

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A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition

arXiv

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arXiv 2024年

作者： Xiong, Qi Zhang, Xinman Shen, Jun International College Hunan University of Arts and Sciences Changde415000 China School of Automation Science and Engineering Faculty of Electronic and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Blind iris images, which result from unknown degradation during the process of iris recognition at long distances, often lead to decreased iris recognition rates. Currently, little existing literature offers a solution to this problem. In response, we propose a prior embedding-driven architecture for long distance blind iris recognition. We first proposed a blind iris image restoration network called Iris-PPRGAN. To effectively restore the texture of the blind iris, Iris-PPRGAN includes a Generative Adversarial network (GAN) used as a Prior Decoder, and a DNN used as the encoder. To extract iris features more efficiently, we then proposed a robust iris classifier by modifying the bottleneck module of InsightFace, which called Insight-Iris. A low-quality blind iris image is first restored by Iris-PPRGAN, then the restored iris image undergoes recognition via Insight-Iris. Experimental results on the public CASIA-Irisdistance dataset demonstrate that our proposed method significantly superior results to state-of-the-art blind iris restoration methods both quantitatively and qualitatively, Specifically, the recognition rate for long-distance blind iris images reaches 90% after processing with our methods, representing an improvement of approximately ten percentage points compared to images without restoration. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Lombard-VLD: Voice Liveness Detection Based on Human Auditory Feedback

Lombard-VLD: Voice Liveness Detection Based on Human Auditor...

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IEEE Symposium on security and Privacy

作者： Hongcheng Zhu Zongkun Sun Yanzhen Ren Kun He Yongpeng Yan Zixuan Wang Wuyang Liu Yuhong Yang Weiping Tu School of Cyber Science and Engineering Wuhan University Wuhan China State Grid Wuhan Electric Power Supply Company Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Voice Liveness Detection (VLD) aims to protect speaker authentication from speech spoofing by determining whether speeches come from live speakers or loudspeakers. Previous methods mainly focus on their differences at the signal level. In this paper, we propose the first VLD that uses the human auditory feedback mechanism (i.e., the Lombard effect), called Lombard-VLD. The key idea is that live speakers can physiologically and involuntarily adjust their speaking patterns in a noisy background but loudspeakers cannot. Moreover, we design a reference-based dual input mode and a differential SE-ResBlock to model the acoustic differences caused by the Lombard effect. Experimental results show that Lombard-VLD achieves 0% and 0.24% EER in two datasets, outperforming the state-of-the-art methods. It is robust to various environmental factors, including different distances, postures of the speaker, and environmental noise, with an average accuracy of over 98.51%. It also has a good generalization to unseen speakers, genders, and datasets, with EER lower than 2.68%, 3.44%, and 7.32%, respectively. This work shows the advantages of the Lombard effect in VLD, which has fewer user limitations and better detection performance.

关键词： Loudspeakers Privacy Accuracy Working environment noise Authentication Physiology Environmental factors Acoustics security Noise measurement

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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Traffic Engineering in Dynamic Hybrid Segment Routing networks

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computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu Jiangyuan Yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

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