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TAKAP: A Lightweight Three-Party Authenticated key Agreement Protocol with User Anonymity

Chinese Journal of Electronics 2021年第4期30卷 686-696页

作者： QIAO Hui DONG Xuewen SHEN Yulong LIU Haozhe YANG Lingxiao School of Computer Science and Technology Xidian University Shaanxi Key Laboratory of Network and System Security Xidian University

The three-party authenticated key agreement protocol is a significant cryptographic mechanism for secure communication, which encourages two entities to authenticate each other and generate a shared session key with the assistance of a trusted party(remote server) via a public channel. Recently, Wang et al. put forward a three-party key agreement protocol with user anonymity and alleged that their protocol is able to resist all kinds of attacks and provide multifarious security features in Computer Engineering & Science, No.3, ***, we show that Wang et al.'s protocol is vulnerable to the password guessing attack and fails to satisfy user anonymity and perfect secrecy. To solve the aforementioned problems, a lightweight chaotic mapbased Three-party authenticated key agreement protocol(short for TAKAP) is proposed, which not only could provide privacy protection but also resist a wide variety of security attacks. Furthermore, it is formally proved under Burrows-Abadi-Needham(BAN) logic. Simultaneously,the performance analysis in this paper demonstrates that the proposed TAKAP protocol is more secure and efficient compared with other relevant protocols.

关键词： Three-party Authenticated key agreement Chaotic map Anonymity security attacks

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Design of distribution transformer monitoring system based on LPC2103

引用

Journal of Convergence Information technology 2012年第21期7卷 120-126页

作者： Sun, Qindong Wang, Qian Wang, Nan Network Computing and Security Key Lab of Shaanxi Xi'an University of Technology Xi'an 710048 China

Aimed at satisfying the monitoring requirement of distribution transformer, a kind of distribution transformer monitoring system based on LPC2103 is designed. Hardware module and data processing program of the monitoring system are introduced. The SD card of large capacity is used to store data collected by the supervising system, which could support the detail analysis of running status of distribution transformer. A simplified design of electricity cut examination is adopted to decrease the complexity and cost of the system. The system has been used in truth circumstance and the performance of system is satisfactory.

关键词： Monitoring

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An algorithm for automatic photo typesetting based on kinship

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Journal of Information and Computational Science 2014年第13期11卷 4727-4736页

作者： Sun, Qindong Wang, Yan Tian, Minying Wang, Qian Zhang, Jingpeng Key Lab for Network Computing and Security of Shaanxi Province Xi'an University of Technology Xi'an China

Photo typesetting belongs to the rectangular packing problems, currently the algorithms of rectangular packing problems can be divided into two categories: heuristic methods and modern intelligent based methods. For both of these methods, the similarity of contents do not be well considered, the packing order of the rectangular pieces are chaos. Therefore, this drawback will cause problems for photo typesetting. In this paper we propose a hybrid algorithm for the photo typesetting problem based on kinship. According to our algorithm, the kinship of different photos is calculated by their absolute paths, and then all photos are packed based on the distance of their kinship. Compared with the existing methods, according to the computation of different scales of tests, our algorithm shows faster typesetting speed and higher utilization ratios. This algorithm could solve the sort problem caused by chaos typesetting photos of different customers after lots of photos were typeset. Our algorithm could greatly improve the practical application of photo typesetting. Copyright © 2014 Binary Information Press.

关键词： Heuristic methods

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Never Lost keys:A Novel key Generation Scheme Based on Motor Imagery EEG in End-Edge-Cloud System

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China Communications 2022年第7期19卷 172-184页

作者： Yichuan Wang Dan Wu Xiaoxue Liu Xinhong Hei School of computer science and Engineering Xi’an University of TechnologyXi’an 710048China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an 710048China

Biometric key is generated from the user’s unique biometric features,and can effectively solve the security problems in ***,the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged *** to the characteristics of Electroencephalographic(EEG)signals such as the randomness,nonlinear and non-stationary etc.,it can significantly avoid these *** paper proposes a novel method to generate keys based on EEG signals with end-edgecloud collaboration *** sensors to measure motor imagery EEG data,the key is generated via pre-processing,feature extraction and *** show the total time consumption of the key generation process is about *** scheme is practical and feasible,which provides a research route to generate biometric keys using EEG data.

关键词： EEG biometric key generation end-edgecloud system information security

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Modeling and analysis of the proactive worm in unstructured Peer-to-Peer network

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Journal of Convergence Information technology 2010年第5期5卷 111-117页

作者： Sun, Qindong Wang, Qian Ren, Jie Network Computing and Security Key Lab of Shaanxi Xi'an University of Technology Xi'an 710048 China

With the popularity of P2P (Peer-to-Peer) network applications, active P2P worms become one of the most important newly threat of the network security. In this paper, the propagation process of active P2P worms are studied by a dynamic model over the overlay topology which modeled as a power-law undirected graph. Firstly, GLP power law generator is used to generate power law topologies to represent unstructured P2P networks. Then the propagation of P2P worms is modeled over the topology. Specifically, discrete-time is adopted to conduct recursion and approximation to describe propagation of proactive P2P worm. In our model, all the probabilities which would happen to all kinds of peers are considered in full. Therefore, both the whole network's status and the single node's action are taken into consideration. So the model we constructed is more likely the truth. Finally, the simulation studies are carried out to prove that the simulation results match the mathematical model well.

关键词： Peer to peer networks

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Structure-based Street Tree Extraction from Mobile Laser Scanning Point Clouds 2022

Structure-based Street Tree Extraction from Mobile Laser Sca...

引用

5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Hao, Wen Zuo, Zhanbin Liang, Wei School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology China Shaanxi Key Laboratory for Network Computing and Security Technology China

ISBN: (纸本)9781450395465

We present an automatic method based on structure analysis for extracting street trees from mobile laser scanning (MLS) data. Tree trunk and canopy can be characterized by the shape information and height above the ground level. Therefore, MLS point clouds are firstly divided into three layers (ground, low layer above the ground, high layer above the ground) with respect to the vertical height. For the points above the ground, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering method is applied to cluster the points into segments and geometrical features are used to extract trunk candidates and canopy candidates. Then, a "structure-matching" strategy is proposed based on minimum bounding rectangle (MBR) mapping to extract the tree candidates. Finally, a slicing method based on axial symmetry is proposed to segment the overlapped canopy. The experimental results show that the proposed method can quickly extract individual trees from massive street point clouds. © 2022 ACM.

关键词： Extraction

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QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Du, Jianbo Gong, Jie Chu, Xiaoli Xiong, Zehui Chen, Xianfu Dong, Mianxiong Yu, F. Richard Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China The University of Sheffield Mappin Street Department of Electronic and Electrical Engineering SheffieldS1 3JD United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore Shenzhen CyberAray Network Technology Company Ltd Shenzhen518000 China Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan Carleton University Systems and Computer Engineering OttawaON Canada

In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms © 2015 IEEE.

关键词： Active learning

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Hierarchical Alignment-enhanced Adaptive Grounding network for Generalized Referring Expression Comprehension

arXiv

引用

arXiv 2025年

作者： Wang, Yaxian Ding, Henghui He, Shuting Jiang, Xudong Wei, Bifan Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University China Institute of Big Data Fudan University China Shanghai University of Finance and Economics China Nanyang Technological University Singapore School of Continuing Education Xi'an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University China

In this work, we address the challenging task of Generalized Referring Expression Comprehension (GREC). Compared to the classic Referring Expression Comprehension (REC) that focuses on single-target expressions, GREC extends the scope to a more practical setting by further encompassing notarget and multi-target expressions. Existing REC methods face challenges in handling the complex cases encountered in GREC, primarily due to their fixed output and limitations in multi-modal representations. To address these issues, we propose a Hierarchical Alignment-enhanced Adaptive Grounding network (HieA2G) for GREC, which can flexibly deal with various types of referring expressions. First, a Hierarchical Multi-modal Semantic Alignment (HMSA) module is proposed to incorporate three levels of alignments, including word-object, phrase-object, and text-image alignment. It enables hierarchical cross-modal interactions across multiple levels to achieve comprehensive and robust multi-modal understanding, greatly enhancing grounding ability for complex cases. Then, to address the varying number of target objects in GREC, we introduce an Adaptive Grounding Counter (AGC) to dynamically determine the number of output targets. Additionally, an auxiliary contrastive loss is employed in AGC to enhance object-counting ability by pulling in multi-modal features with the same counting and pushing away those with different counting. Extensive experimental results show that HieA2G achieves new state-of-the-art performance on the challenging GREC task and also the other 4 tasks, including REC, Phrase Grounding, Referring Expression Segmentation (RES), and Generalized Referring Expression Segmentation (GRES), demonstrating the remarkable superiority and generalizability of the proposed HieA2G. Copyright © 2025, The Authors. All rights reserved.

关键词： Electric grounding

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Cramér-Rao Bound Minimization for a Passive Sensing Multi-ISAC System

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IEEE Transactions on Vehicular technology 2025年

作者： Chen, Jian Chen, Qikai Deng, Yansha Jia, Jie Yin, Baoxin Wang, Xingwei Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China King's College London LondonWC2R 2LS United Kingdom

The integrated sensing and communication (ISAC) system can simultaneously provide communication and radar sensing, effectively improving spectrum utilization. However, existing research mainly focuses on the mono-static sensing ISAC system, which has shortcomings in limited sensing range and accuracy. We thus investigate a passive sensing multi-ISAC system with multiple communication users and sensing targets, and consider two cases for multi-target sensing without and with prior target knowledge at the base stations (BSs), in which the BSs are interested in estimating the targets' response matrix and channel coefficients, respectively. We also formulate a joint beamforming problem to minimize the Cramér-Rao bound (CRB) of the worst estimation constrained by communication requirements, and obtain the optimal solution with semidefinite relaxation (SDR) assisted transformation. To verify the advantages of our proposed scheme, we consider the bi-static BSs system (B-B) and beamforming with sensing interference cancellation (W-S) as two benchmark schemes. Numerical results illustrate that the proposed scheme obtains better sensing performance and effectively alleviates the effect caused by sensing interference. © 1967-2012 IEEE.

关键词： Cramer-Rao bounds

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Scale-aware triple semantic deep network for polarimetric SAR image classification

引用

Applied Soft computing 2025年 177卷

作者： Haiyan Jin Junfei Shi Linjing Xu Mengmeng Nie Tiansheng He Junhuai Li Maoguo Gong Xi’an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology NO.5 South Jinhua Road Xi’an Shaanxi China School of Electronic Engineering Xidian University No. 2 South TaiBai Rood Xi’an 710071 China

Deep learning has demonstrated outstanding performance in polarimetric synthetic aperture radar (PolSAR) image classification. However, complex PolSAR scenes typically contain terrain objects of varying scales and orientations, and existing methods that rely on a single network architecture struggle to effectively capture both multi-scale terrain objects and precise edges. To address this challenge, we propose a scale-aware triple semantic deep network for PolSAR image classification, which is a novel “divide and conquer” framework. This network divides the scene into homogeneous, heterogeneous, and boundary regions, and then customizes specific subnetwork to learn scale-aware features for each region. For the boundary regions, a simple convolutional neural network (CNN) is designed to capture local details effectively. For homogeneous regions, a superpixel-based graph convolutional network (SGCN) is utilized to extract contextual features. For heterogeneous objects, a larger-scale c -hop SGCN is developed to capture global semantic information. These subnetworks are integrated with an attention mechanism to select relevant features and minimize redundancy. Experiments on four real PolSAR datasets demonstrate that our method outperforms state-of-the-art approaches, particularly in classifying heterogeneous objects and achieving precise edges.

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