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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Semi-auto Cross-Boundary Mesh Segmentations

Semi-auto Cross-Boundary Mesh Segmentations

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第九届中国图学大会

作者： Jiguang Lian Yuze Shi Haisheng Li Nan Li Xiaochuan Wang Beijing Technology and Business University Beijing Key Laboratory of Big Data Technology for Food Safety National Engineering Laboratory For Agri-product Quality Traceability

3D segmentation has been a hot spot in numerical geometry *** the accuracy of the segmentation methods can be easily affected by the types of models because their sensitivity to different models is *** address this problem,we propose a semi-automatic 3D mesh segmentation algorithm based on harmonic ***,our algorithm utilizes the strokes of users as constraints on the harmonic field of the mesh ***,a smooth harmonic field based on Laplacian operator and Poisson equation is *** the generated harmonic field,the correct weights are selected to further fit the geometric characteristics of the ***,we find a set of most suitable isolines on the harmonic field as the segmentation ***,a mesh density enhancement method is designed,which optimizes sub-graphs after *** results demonstrate that the effectiveness of our proposed ***,the semi-automatic 3D mesh segmentation algorithm can better understand the intention of users.

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Trustworthy preference completion in social choice

arXiv

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arXiv 2020年

作者： Li, Lei Xue, Minghe H., Chen X., Wu The School of Computer Science and Technology University of Science and Technology of China China Mininglamp Academy of Sciences Mininglamp Technologies Beijing China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China

As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion. Specifically, the personalized preference of each agent over all the alternatives can be estimated with partial rankings from neighboring agents over subsets of alternatives. However, since the agents’ rankings are nondeterministic, where they may provide rankings with noise, it is necessary and important to conduct the trustworthy preference completion. Hence, in this paper firstly, a trust-based anchor-kNN algorithm is proposed to find k-nearest trustworthy neighbors of the agent with trust-oriented Kendall-Tau distances, which will handle the cases when an agent exhibits irrational behaviors or provides only noisy rankings. Then, for alternative pairs, a bijection can be built from the ranking space to the preference space, and its certainty and conflict can be evaluated based on a well-built statistical measurement Probability-Certainty Density Function. Therefore, a certain common voting rule for the first k trustworthy neighboring agents based on certainty and conflict can be taken to conduct the trustworthy preference completion. The properties of the proposed certainty and conflict have been studied empirically, and the proposed approach has been experimentally validated compared to state-of-arts approaches with several data sets. © 2020, CC BY.

关键词： Nearest neighbor search

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Evolutionary Dynamics Modeling of Symbolic Social Network Structure Equilibrium

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China Communications 2020年第10期17卷 229-240页

作者： Weijin Jiang Sijian Lv Yirong Jiang Jiahui Chen Fang Ye Xiaoliang Liu College of Computer and Information Engineering Hunan University of Technology and BusinessChangsha 410205China Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and BusinessChangsha 410205China Institute of Big Data and Internet Innovation Mobile E-business Collaborative Innovation Center of Hunan ProvinceHunan University of CommerceChangsha 410205China Tonghua Normal University Tonghua 134002China

The use of symbol attributes on the side of symbolic social networks to analyze,understand,and predict the topology,function,and dynamic behaviour of complex networks,and has important theoretical significance for personalized recommendations,attitude prediction,user feature analysis,and clustering and application ***,due to the huge scale of online social networks,this poses a challenge to traditional symbolic social network analysis *** on the theory of structural equilibrium,this paper studies the evolutionary dynamics of symbolic social networks,proposes the energy function of weak structural equilibrium theory,and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the *** simulation experiment results show that the calculation method in this paper can get the optimal solution *** provides an idea for the study of real and complex social networks.

关键词： incremental calculation symbolic network weak structure equilibrium evolutionary algorithms

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A Lightweight Experimental Platform for Big data Based on Docker Containers

A Lightweight Experimental Platform for Big Data Based on Do...

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作者： Gu Ruijun Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of Technology School of Computer Engineering Jinling Institute of Technology

In recent years, many colleges and universities have set up the major of big *** biggest problem in teaching is that there is no supporting basic experimental environment,and it is difficult to deploy and configure the big data environment at the same time. In addition, the lack of experimental data, experimental teaching plans and experimental manuals in the experimental process makes it difficult to carry out relevant teaching. In order to reduce the cost of laboratory construction and the difficulty of learning big data, a lightweight big data experimental platform was constructed based on virtualized container technology. Through this platform, we can create a big data cluster, provide various suitable experimental environments,focus on the technology itself, and greatly improve the learning efficiency.

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Optimization of a Deep Learning Algorithm for Security Protection of Big data from Video Images

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Computational Intelligence and Neuroscience 2022年第0期2022卷 3394475页

作者： Geng, Qiang Yan, Huifeng Lu, Xingru School of Big Data and Software Engineering Chongqing College of Mobile Communication Chongqing401520 China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob's decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve's classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image. © 2022 Qiang Geng et al.

关键词： Cryptography

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Vehicle appearance feature recognition based on image

Vehicle appearance feature recognition based on image

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and Computer(MMEBC 2016)

作者： Deyang Shao Chao Xu Shixian Luo Bo Feng Long Jiao Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Key Laboratory of Intelligent Computer & Signal Processing Ministry of EducationAnhui University

ISBN: (纸本)9781510828087

Now, the highway toll system still uses a single license plate recognition, this method has a problem of inaccurate identificationFor this kind of situation, this paper put forward to increase the appearance of the vehicle feature information and can improve the accuracy of recognitionIn this paper, we adopt the ORB algorithm to extract the exterior feature information of the vehicle and two-way matching、RANSAC algorithms to remove mismatching pointsAt the same time, we continue to iteration the scale parameter of the affine transformation and rotation angle at the matching point as a kind of judgment, which improves the robustness of the algorithm.

关键词： Feature information ORB RANSAC Affine transformation

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KAN v.s. MLP for Offline Reinforcement Learning

arXiv

引用

arXiv 2024年

作者： Guo, Haihong Li, Fengxin Li, Jiao Liu, Hongyan School of Information Renmin University of China China Institute of Medical Information Medical Library Chinese Academy of Medical Sciences Peking Union Medical College China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Economics and Management Tsinghua University China

Kolmogorov-Arnold Networks (KAN) is an emerging neural network architecture in machine learning. It has greatly interested the research community about whether KAN can be a promising alternative of the commonly used Multi-Layer Perceptions (MLP). Experiments in various fields demonstrated that KAN-based machine learning can achieve comparable if not better performance than MLP-based methods, but with much smaller parameter scales and are more explainable. In this paper, we explore the incorporation of KAN into the actor and critic networks for offline reinforcement learning (RL). We evaluated the performance, parameter scales, and training efficiency of various KAN and MLP based conservative Q-learning (CQL) on the the classical D4RL benchmark for offline RL. Our study demonstrates that KAN can achieve performance close to the commonly used MLP with significantly fewer parameters. This provides us an option to choose the base networks according to the requirements of the offline RL tasks. © 2024, CC BY.

关键词： Adversarial machine learning

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SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Song, Zhenbo Liu, Hongyan Lu, Zhiwu He, Jun Du, Xiaoyong Key Laboratory of Data Engineering Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Management Science and Engineering Tsinghua University Beijing100084 China

Simultaneous Localization and Mapping (SLAM) and Autonomous Driving are becoming increasingly more important in recent years. Point cloud-based large scale place recognition is the spine of them. While many models have been proposed and have achieved acceptable performance by learning short-range local features, they always skip long-range contextual properties. Moreover, the model size also becomes a serious shackle for their wide applications. To overcome these challenges, we propose a super light-weight network model termed SVT-Net. On top of the highly efficient 3D Sparse Convolution (SP-Conv), an Atom-based Sparse Voxel Transformer (ASVT) and a Cluster-based Sparse Voxel Transformer (CSVT) are proposed respectively to learn both short-range local features and long-range contextual features. Consisting of ASVT and CSVT, SVT-Net can achieve state-of-the-art performance in terms of both recognition accuracy and running speed with a super-light model size (0.9M parameters). Meanwhile, for the purpose of further boosting efficiency, we introduce two simplified versions, which also achieve state-of-the-art performance and further reduce the model size to 0.8M and 0.4M respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Dual Encoding for Zero-Example Video Retrieval

Dual Encoding for Zero-Example Video Retrieval

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Jianfeng Dong Xirong Li Chaoxi Xu Shouling Ji Yuan He Gang Yang Xun Wang College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba Group

ISBN: (纸本)9781728132945

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval.

关键词： Videotapes video retrieval End users end-to-end Dual coding

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