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Design of Provably Secure Authentication Protocol for Edge-Centric Maritime Transportation System

IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 14536-14545页

作者： Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Ghaffar, Zahid Khan, Muhammad Khurram Das, Ashok Kumar National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol's security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols. © 2000-2011 IEEE.

关键词： Hash functions

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Edge Detection Algorithm for Urban Road Images Based on Deep Learning 3

Edge Detection Algorithm for Urban Road Images Based on Deep...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2024

作者： Zhou, Mo Zhang, Li School Of Electrical And Electronic Engineering Wuhan Institute Of Shipbuilding Technology Wuhan China School Of Computer Science Wuhan Qingchuan University Wuhan China

ISBN: (纸本)9798350318609

Conventional algorithms demonstrate poor performance in complex urban road settings. Factors like noise, shadows, and changes in the composition of the road surface in photos can erode the accuracy of traditional algorithms, making them less reliable in actual city settings. The accuracy of edge detection in photographs of urban roads is improved by the incorporation of an effective neural network. This enhancement makes it possible for the algorithm to more accurately capture the intricate details and geometric structure of highways. In order to preserve the original image quality, this study preprocesses a sizable amount of urban road image data. The establishment of an efficient neural network involves constructing a network with an Encoder for image downsampling. The bottleneck comprises multiple residual blocks, and an Encoder is employed for image upsampling. © 2024 IEEE.

关键词： Edge detection

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Monitoring Recombinant Protein Titer in Escherichia Coli Fermentations Using Filtering Techniques

Monitoring Recombinant Protein Titer in Escherichia Coli Fer...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Okafor, Paul Siddique, Zahed Razzaghi, Talayeh University of Oklahoma Data Science and Analytics Institute Norman United States University of Oklahoma School of Aerospace and Mechanical Engineering Norman United States University of Oklahoma School of Industrial and Systems Engineering Norman United States

ISBN: (纸本)9798350362480

Recombinant protein production in Escherichia coli (E. coli) is crucial for biotechnological applications, but optimizing protein titer is challenging and often requires iterative experimentation and expert guidance. This study utilizes advanced filtering techniques - Kalman, particle, bilateral, and extended Kalman filters - to enhance titer estimation accuracy using fermentation data provided by Cytovance Biologics. The proposed techniques effectively impute missing titer values and are validated on a secondary dataset from Chinese Hamster Ovary (CHO) cell lines, demonstrating robustness and accuracy. By reducing the need for extensive sampling and iterative experimentation, these filtering techniques enable predictive control over fermentation conditions. One significant cost-saving advantage is identifying the protein titer plateau, allowing lab scientists to conclude the fermentation process at the optimal time. This approach ensures maximum protein production while preventing unnecessary resource consumption. As a result, this method significantly lowers the overall cost of recombinant protein production in E. coli while enhancing process efficiency. © 2024 IEEE.

关键词： Wiener filtering

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MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4695-4706页

作者： Haq, Amin ul Li, Jian Ping Kumar, Rajesh Ali, Zafar Khan, Inayat Uddin, M. Irfan Agbley, Bless Lord Y. Sichuan Chengdu611731 China University of Electronic Science and Technology of China Huzhou313001 China School of Computer Science and Engineering Southeast University Nanjing210096 China Department of Computer Science University of Buner Buner19290 Pakistan Institute of Computing Kohat University of Science and Technology Kohat26000 Pakistan

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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Heterogeneous Device Collaboration Based Federated Learning for Big data Applications

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IEEE Transactions on Big data 2024年 1-11页

作者： Wang, Wenhua Yang, Quan Liang, Yuzhu Xu, Yang Liu, Qin Wang, Tian School of Artificial Intelligence Beijing Normal University Beijing China College of Computer Science and Technology Huaqiao University Xiamen China College of Computer Science and Electronic Engineering Hunan University Changsha China BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

In the era of Big data, artificial intelligence and information science are the key technologies to extract the value of data and enhance the competitiveness of enterprises. The characteristics of distributed, small-scale, and sparse lead to the isolated data island problem. To solve these problems, Federated Learning is proposed. However, a large number of terminal models need to be uploaded to the server in Federated Learning, especially for the actual scenario of Internet of Things. Therefore, huge communication costs are required which dramatically increases the pressure on the backbone network. Furthermore, the low quality of the local model will lead to decreased accuracy and convergence rates of the model. To overcome the above limitations, we propose heterogeneous device collaboration based federated learning (HDCFL), which constructs a three-layer structure for Federated Learning by leveraging edge computing and designs a heterogeneous device collaboration method that groups the terminals based on their computing power, communication time, and data volume to train the model. Then, we conduct a theoretical analysis of the proposed algorithm which verifies its advantage. At last, the experimental result demonstrates that the proposed algorithm consistently achieves superior performance in terms of both convergence speed and accuracy compared with state-of-the-art baselines. IEEE

关键词： Competition

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of Earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques

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Intelligent Automation & Soft Computing 2023年第8期37卷 1325-1344页

作者： Abdus Saboor Arif Hussain Bless Lord Y。Agbley Amin ul Haq Jian Ping Li Rajesh Kumar Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China Abdul Wali Khan University Mardan Mardan23200Pakistan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Brain Institute Peshawar Peshawar25130Pakistan

Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult *** the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of *** recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research *** this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+*** methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE *** results were compared to other machine learning models such as support vector regression,random forest,and k-nearest *** best results for the three datasets were obtained by the RNN-based models combined with the attention *** performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.

关键词： Machine learning deep learning stock market prediction data analysis

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Learning Enhanced Representations via Contrasting for Multi-view Outlier Detection 28th

Learning Enhanced Representations via Contrasting for Mult...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Xiaocong Wang, Xinye Wang, Yang Han, Chao Duan, Lei School of Computer Science Sichuan University Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Nuclear Power Institute of China Chengdu China

ISBN: (纸本)9783031306778

Multi-view outlier detection has attracted rapidly growing attention to researchers due to its wide applications. However, most existing methods fail to detect outliers in more than two views. Moreover, they only employ the clustering technique to detect outliers in a multi-view scenario. Besides, the relationships among different views are not fully utilized. To address the above issues, we propose ECMOD for learning enhanced representations via contrasting for multi-view outlier detection. Technically, ECMOD leverages two channels, the reconstruction and the constraint view channels, to learn the multi-view data, respectively. The two channels enable ECMOD to capture the rich information better associated with outliers in a latent space due to fully considering the relationships among different views. Then, ECMOD integrates a contrastive technique between two groups of embeddings learned via the two channels, serving as an auxiliary task to enhance multi-view representations. Furthermore, we utilize neighborhood consistency to uniform the neighborhood structures among different views. It means that ECMOD has the ability to detect outliers in two or more views. Meanwhile, we develop an outlier score function based on different outlier types without clustering assumptions. Extensive experiments on real-world datasets show that ECMOD significantly outperforms most baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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