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11th International Conference on Information Technology: IoT and Smart City, ICIT 2023

作者： Cai, Jiancheng Zou, Zheng Wu, Yi Liu, Shijian Liao, Shenghui College of Computer and Cyber Security Fujian Normal University Fuzhou China School of Computer Science and Mathematics Fujian University of Technology Fuzhou China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798400709043

Accurate occlusal tooth segmentation for dental CBCT is essential for traumatic occlusal force diagnosis. As tiny multi-objects, adhesion from surrounding touching would largely hinder the correct detection. Especially, the convex and concave occlusal surface appears irregular and unconnected and increase the difficulties for separation. We proposed a three-stage segmentation framework. First, the panoramic view is incorporated for better dental spatial relationship understanding. Second, a tracking based on heuristic shape is specially designed for the adhesions among occlusal crown. Finally, the heuristic shape is further encoded as an attention in an improved 3DUnet to enhance the segmentation accuracy. To get the specific features of our dataset without large-scale samples, we propose a strategy from 2D coarse to 3D fine segmentation to reduce the unnecessary computation. In experiments, comparison and ablation studies demonstrate that our method generates more accurate results and outperforms other three state-of-The-Art segmentations. © 2023 ACM.

关键词： Semantics

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AntiMal: an Approach of Malware Detection Employing Swin Transformer 13

AntiMal: an Approach of Malware Detection Employing Swin Tra...

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13th International Conference on Communication and Network security, ICCNS 2023

作者： Lyu, Chenxi Yao, Yinan Guo, Xiaodong Huang, Zihong Dong, Chen Zhang, Yuanyuan Chen, Zhenyi College of Computer and Data Science Fuzhou University Fuzhou China College of Computer and Cyber Security Fujian Normal University Fuzhou China Department of Computer Science and Engineering University of South Florida FL United States

ISBN: (纸本)9798400707964

In the present era, the menace of malicious software is growing continuously, posing a significant challenge in the realm of cybersecurity. Currently, traditional machine learning algorithms have been widely applied to the analysis of malicious software and are considered effective methods. However, these approaches often require extensive feature engineering, feature learning, and feature representation, which can be time-consuming and resource-intensive. In this paper, a method for the identification of malicious software, named "AntiMal,"is proposed. This method utilizes the Swin Transformer as the backbone network and combines it with image features for static analysis. It leverages the dataset provided by Microsoft for the Kaggle Microsoft Malware Classification Challenge. Initially, it converts the binary data of malicious code into grayscale images, treating every 8 bits as a pixel. Subsequently, it employs PyTorch to build and train the Swin Transformer, classifying the images in the validation set. The results demonstrate that this approach excels in the problem of malicious software classification, achieving an impressive accuracy of up to 95%, showcasing remarkable precision and generalization capabilities. Furthermore, it exhibits significant advantages when dealing with large datasets, requiring fewer computational resources, thus offering exceptional computational efficiency. © 2023 ACM.

关键词： Malware

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Perception System of Autonomous Vehicles: Sophisticated Attacks, Defense Strategies, and Future Directions 15

Perception System of Autonomous Vehicles: Sophisticated Atta...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neethu, M.R. Remyakrishnan, P. Mukesh, Sa Barath Kumar, J. Royal, Ala Manas Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore641112 India Amrita Vishwa Vidyapeetham TIFAC-CORE in Cyber Security Amrita School of Engineering Coimbatore641112 India

ISBN: (纸本)9798350370249

The advent of autonomous vehicles marks a significant milestone in the evolution of automotive technologies. Central to the operation of autonomous vehicles is the perception system that interprets the surroundings and makes decisions. The security of perception systems against malicious attacks is a critical concern. This work thoroughly analyzes the vulnerabilities inherent in the perception systems of autonomous vehicles, examining the multifaceted threats posed by adversaries. The crucial need for ongoing research in AV security is underscored and a framework for future investigations to develop more secure and trustworthy autonomous vehicles is provided. The analysis emphasizes the importance of a proactive approach to security, advocating for integrating advanced defense mechanisms as a fundamental component of autonomous vehicle design and development and future research challenges. © 2024 IEEE.

关键词： Autonomous Vehicles Camera Lidar Perception Systems security attacks

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Federated Learning with Blockchain for Privacy-Preserving Data Sharing in Internet of Vehicles

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China Communications 2023年第3期20卷 69-85页

作者： Wenxian Jiang Mengjuan Chen Jun Tao School of Cyber Science and Engineering Southeast UniversityNanjing 211189China College of Computer Science and Technology Huaqiao UniversityXiamen 361021China Key Lab of CNII MOESoutheast UniversityNanjing 211189China Purple Mountain Laboratories:Networking Communications and Security Nanjing 211111China

Data sharing technology in Internet of Vehicles(Io V)has attracted great research interest with the goal of realizing intelligent transportation and traffic ***,the main concerns have been raised about the security and privacy of vehicle *** mobility and real-time characteristics of vehicle data make data sharing more difficult in Io *** emergence of blockchain and federated learning brings new *** this paper,a data-sharing model that combines blockchain and federated learning is proposed to solve the security and privacy problems of data sharing in Io ***,we use federated learning to share data instead of exposing actual data and propose an adaptive differential privacy scheme to further balance the privacy and availability of ***,we integrate the verification scheme into the consensus process,so that the consensus computation can filter out low-quality *** data shows that our data-sharing model can better balance the relationship between data availability and privacy,and also has enhanced security.

关键词： blockchain federated learning privacy data sharing Internet of Vehicles

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Volume Hiding Conjunctive Searchable Symmetric Encryption over Cloud 16

Volume Hiding Conjunctive Searchable Symmetric Encryption ov...

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16th International Conference on cyber-Enabled Distributed Computing and Knowledge Discover, cyberC 2024

作者： Deng, Qinghua Chen, Lanxiang Zhu, Yizhao Mu, Yi City University of Macau Faculty of Data Science 999078 China Hanshan Normal University School of Computer and Information Engineering Chaozhou521041 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou China

ISBN: (纸本)9798331506896

Symmetric searchable encryption (SSE) is crucial for promoting cloud storage. It enables users to encrypt private data and outsource it to untrusted servers while retaining query capabilities. To prevent the cloud from obtaining users' private data, researchers have proposed numerous schemes to protect users' privacy. However, the issue of volume hiding has received less attention. Recent studies have demonstrated that volume leakage can be exploited to infer query *** mitigate the aforementioned leakage problem, we introduce an efficient volume-hiding SSE scheme supporting conjunctive queries, denoted as VHXT. The primary strategy for implementing volume hiding comprises two aspects: first, employing a bitmap-based inverted index to obscure the query response length of the least frequent keyword, and second, introducing laconic PSI for efficient membership testing. The laconic PSI enables our scheme to achieve approximately optimal overhead. Moreover, security analysis and performance evaluation demonstrate that VHXT effectively prevents volume leakage with minimal overhead. © 2024 IEEE.

关键词： Information leakage

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Enhanced Prediction of Swimmer Fitness Using Modified Resilient PSO Algorithm

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Transactions of the Indian National Academy of engineering 2024年第4期9卷 903-915页

作者： Geetha Poornima, K. Krishna Prasad, K. Institute of Computer Science and Information Science Srinivas University Mangalore India Cyber Security and Cyber Forensics in the Institute of Engineering and Technology Srinivas University Mangalore India

Sports develop both physical and mental growth of an individual. In order to enhance the physical and mental health effectively and effortlessly, swimming is considered to be one of the healthier activities, in developing the metabolism via flexibility, weight loss, reducing asthma and enhancing the fitness levels of the body. In most of the cases, existing studies provide the current status of the swimmer by delivering details like time taken by the swimmers to finish the lap or location of the swimmer, however they lack in identifying the calorie level reduction of the swimmer during the swimming, as, the detection of calories burnt by the swimmer helps in overcoming issues like loss of weight and other health risk rates. Therefore, proposed study intensifies in achieving the prediction of the fitness of a swimmer as the primitive activity in view of reducing the calories by using PSO algorithm using resilient based techniques for enhanced exploration ability and using modified resilient PSO in order to tune the hyper parameter of the random forest for better optimization outcome. Modified resilient PSO helps in preventing the over-fitting of the model and delivering enhanced global solutions. Besides, hyper parameter are tuned in the Random Forest (RF), Decision Tree (DT), AdaBoost and Support Vector Machine (SVM) regression model for an error free outcome and with satisfactory performance analytical values. Finally, the performance of the proposed model is assessed using performance metrics such as R-square, RMSE, MSE and MAE, unveiling the effectiveness of the projected approach MPSO-XG-Boost. The projected model has achieved effectual outcomes encompassing less error rate based outcomes in ranges of 0.037, 0.191, 0.059, 0.9. Further, the proposed is compared with the existing models in order to determine the efficiency of the proposed framework.

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A Payment Channel Network Fee Allocation Strategy Integrating Auction Theory

A Payment Channel Network Fee Allocation Strategy Integratin...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Zhang, Jingjing Xiao, Shanbin Wu, Weigang Zhou, Jieying Guangdong University of Foreign Studies School of Cyber Security Guangzhou China Sun Yat-sen University School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350333336

The payment channel network is an auspicious approach to improve the blockchain scalability, by allowing multi-hop off-chain payments between any two nodes. Therefore, a good incentive mechanism is very important to encourage nodes to actively assist multi-hop payments. However, the current fee allocation strategies do not allocate the fee reasonably and lacks the actual incentive *** this paper, we introduce the auction theory, and propose the second-price dynamic auction fee allocation strategy. This strategy includes node pricing and fee allocation. Specially, the pricing of nodes is determined by six parts: base fee, proportional fee, skew fee, density fee, time fee and other fees. The nodes in the path obtain fees according to its own bid and the lowest bid and the second lowest bid in the auction. Additionally, we find that there is an incentive explosion problem in the fee allocation strategy determined by intermediate nodes, and proposes a virtual fee mechanism to alleviate this problem. The simulation results show that our second-price dynamic auction fee allocation strategy has more fair allocation performance than the Lightning Network fee allocation strategy, and has the ability to resist incentive explosion. © 2023 IEEE.

关键词： Blockchain

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Knowledge-based recommendation with contrastive learning

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High-Confidence Computing 2023年第4期3卷 41-46页

作者： Yang He Xu Zheng Rui Xu Ling Tian School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Kash Institute of Electronics and Information Industry Kashgar 844199China China Electronic Technology Cyber Security Co. Ltd.Chengdu 610041China Cyberspace Security Key Laboratory of Sichuan Province Chengdu 610041China

Knowledge Graphs(KGs)have been incorporated as external information into recommendation systems to ensure the high-confidence ***,Contrastive Learning(CL)framework has been widely used in knowledge-based recommendation,owing to the ability to mitigate data sparsity and it considers the expandable computing of the ***,existing CL-based methods still have the following shortcomings in dealing with the introduced knowledge:(1)For the knowledge view generation,they only perform simple data augmentation operations on KGs,resulting in the introduction of noise and irrelevant information,and the loss of essential information.(2)For the knowledge view encoder,they simply add the edge information into some GNN models,without considering the relations between edges and ***,this paper proposes a Knowledge-based Recommendation with Contrastive Learning(KRCL)framework,which generates dual views from user–item interaction graph and ***,through data enhancement technology,KRCL introduces historical interaction information,background knowledge and item–item semantic ***,a novel relation-aware GNN model is proposed to encode the knowledge ***,through the designed contrastive loss,the representations of the same item in different views are closer to each *** with various recommendation methods on benchmark datasets,KRCL has shown significant improvement in different scenarios.

关键词： Knowledge graph Recommendation systems Contrastive learning Graph neural network

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Malicious Traffic Detection with Noise Labels Based on Cross-Modal Consistency

IEEE Networking Letters

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IEEE Networking Letters 2024年第2期6卷 148-151页

作者： Yuan, Qingjun Niu, Weina Wang, Yongjuan Gou, Gaopeng Lu, Bin Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives. © 2019 IEEE.

关键词： Feature extraction

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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