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Security Challenges and Potential Solutions in Aerial-Terrestrial Wireless Networking

IEEE Internet of Things Magazine 2023年第4期6卷 118-123页

作者： Sood, Keshav Nguyen, Dinh Duc Nha Qu, Youyang Cui, Lei Karmakar, Kallol Krishna Yu, Shui School of Information Technology Deakin University Australia Data61 Australia Shandong Computer Science Center National Supercomputer Center in Jinan China The University of Newcastle Australia University of Technology Sydney Australia

Integration of unmanned aerial vehicles (UAVs) with terrestrial networks form an aerial-terrestrial wireless networking concept. Particularly, the UAVs are composed of Internet of Things sensors (IoTs) and they also are typically known as flying IoTs. These are used to collect real-time data for diverse network's applications in order to make optimal network performance and secure management decisions. In this networking domain, cyber-attacks on UAVs sensors are rapidly increasing and the attacks on sensors inevitably raise the data sparsity issue, means the recorded data, by drones or UAVs, could be often incomplete and even sparse enough too. This impacts the performance eval-uation of this networking composed of flying and ground base stations. This paper explores the data sparsity issue in context of the integration of aerial-terrestrial wireless networks. Critical challenges and potential high-level solutions are also proposed to enhance the security and performance of networks. © 2018 IEEE.

关键词： Antennas

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StudentCoin Price Prediction and Relation to Blockchain Education 10

StudentCoin Price Prediction and Relation to Blockchain Educ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kaur, Rashmeet Sonia Iorio, Carmela University of Naples Federico Ii Department of Economics Science and Statistics Naples Italy Yogananda School of AI Computer and Data Science Shoolini University Solan India

ISBN: (纸本)9789380544472

The newly introduced cryptocurrency called StudentCoin which is identified by the acronym STC is a blockchain-based project. It is the first platform in the world for easy tokenization. It allows the users to learn about cryptocurrencies, blockchain technology and their use cases. Users can create their own tokens, coins and also trade them on the same terminal. This paper uses easy-to-understand modeling and simulation techniques to make price predictions for blockchain education-oriented cryptocurrencies like StudentCoin. We have used the Monte Carlo simulation technique to determine if the price of StudentCoin is expected to increase within a month, and that combining education with blockchain and cryptocurrency platforms is beneficial. The study also aims to find out if college students are aware of cryptocurrencies and see if it's possible to combine education and cryptocurrency. The paper contributes to promoting awareness among university students about responsible investment practices in the cryptocurrency market. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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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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Interactive Visualization of Temporal Brain Connectivity data based-on Frequent Feature Mining 35

Interactive Visualization of Temporal Brain Connectivity Dat...

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Guangwei Jing, Ming Liu, Yunjing Zhang, Li Dong, Anming Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure. In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest. To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas. This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas. © 2023 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Brain

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A Novel Adaptive DCT Filter for Image Manipulation Localization 5

A Novel Adaptive DCT Filter for Image Manipulation Localizat...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Wang, Haichou Cheng, Hang Chen, Jiabin Xu, Yongliang School of Mathematics and Statistics Fuzhou University Fujian Province Fuzhou350108 China College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China

ISBN: (纸本)9798331530334

Image Manipulation Localization (IML) is a fundamental binary segmentation task, focused on the precise identification and demarcation of the manipulated regions within an image. Most existing models primarily rely on RGB and noise features to accurately identify the tampered areas in images. However, in practical IML tasks, the effectiveness of commonly used noise feature modules is often compromised by the unknown tampering methods and the diversity of images. In this paper, we design an adaptive filter based on the Discrete Cosine Transform (DCT) to localize the manipulated regions. Furthermore, we theoretically demonstrate that the adaptive filter is equivalent to convolving the image with a large-scale convolutional kernel, thereby taking full account of features across the entire image. Through extensive experimentation on several benchmark image tampering datasets, our model has demonstrated performance that rivals the most state-of-the-art methods. © 2024 IEEE.

关键词： Adaptive filtering

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Personalized Review Recommendation based on Implicit dimension mining

Personalized Review Recommendation based on Implicit dimensi...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Xu, Bei Xu, Yifan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of "text + dimension". The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding "text + dimension" is better than encoding "text" and "dimension" separately in review recommendation. © 2024 Association for Computational Linguistics.

关键词： Signal encoding

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Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models 30

Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-traine...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Keyaki, Atsushi Keyaki, Ribeka Dapartment of Social Data Science Hitotsubashi University 2-1 Naka Kunitachi Tokyo Japan School of Computer Science Tokyo University of Technology 1404-1 Katakuramachi Hachioji City Tokyo Japan

ISBN: (纸本)9782493814104

Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations and query-document relations in coarse-tuning, we aim to reduce the load of fine-tuning and improve the learning effect of downstream IR tasks. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs. Evaluation experiments show that the proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. Furthermore, the results of the query prediction task suggested that coarse-tuning facilitated learning of query representation and query-document relations. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Information retrieval systems

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Hybrid RNN Based Text Classification Model for Unstructured data

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SN computer science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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Deep Learning-Driven Black Box Approach For Image Segmentation 1

Deep Learning-Driven Black Box Approach For Image Segmentati...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： Image segmentation

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Parallel Segment Entanglement Swapping 1

Parallel Segment Entanglement Swapping

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1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024

作者： He, Binjie Loke, Seng W. Zhang, Dong College of Computer and Data Science Fuzhou University Fuzhou China School of Information Technology Deakin University Melbourne Australia Zhicheng College Fuzhou University Fuzhou China

ISBN: (纸本)9798350366778

In the noisy intermediate-scale quantum era, scientists are trying to improve the entanglement swapping success rate by researching anti-noise technology on the physical level, thereby obtaining a higher generation rate of long-distance entanglement. However, we may improve the generation rate from another perspective, which is studying an efficient entanglement swapping strategy. This paper analyzes the challenges faced by existing entanglement swapping strategies, including the node allocation principle, time synchronization, and processing of entanglement swapping failure. We present Parallel Segment Entanglement Swapping (PSES) to solve these problems. The core idea of PSES is to segment the path and perform parallel entanglement swapping between segments to improve the generation rate of long-distance entanglement. We construct a tree-like model as the carrier of PSES and propose heuristic algorithms called Layer Greedy and Segment Greedy to transform the path into a tree-like model. Moreover, we realize the time synchronization and design the on-demand retransmission mechanism to process entanglement swapping failure. The experiments show that PSES performs superiorly to other entanglement swapping strategies, and the on-demand retransmission mechanism can reduce the average entanglement swapping time by 80% and the average entanglement consumption by 80%. © 2024 IEEE.

关键词： Quantum communication

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