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Cost-Effective PUF and ECC Based Authentication Protocol for Secure Internet of Drones Communication

IEEE Internet of Things Journal 2025年

作者： Nyangaresi, Vincent Omollo Ahmad, Musheer Maghrabi, Louai A. Althaqafi, Turki Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bondo Kenya Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Electronics Chennai India Jamia Millia Islamia Department of Computer Engineering New Delhi 110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia Dar Al-Hekma University School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia

Internet of Drones (IoD) plays a critical role in remote monitoring operations. It has been heavily deployed in applications such as traffic management, air traffic control, disaster and agricultural monitoring. In such operations, high volumes of sensitive and private data is collected and transmitted to the ground stations. The usage of unreliable and open wireless channels during the message exchange process exposes the IoD to a myriad of security threats. Therefore, numerous authentication protocols have been developed in the recent past to mitigate these threats. However, most of these protocols are still inadequate in the provision of robust security against typical IoD attacks. Because, they involve resource-intensive operations and make them unsuitable for an IoD environment. Therefore, we put forward a new authentication protocol which exhibits costeffective performance. To develop the new protocol for secure IoD communication, we leverage on biometrics, physically unclonable function and elliptic curve cryptography for the robust performance. Extensive formal security analysis using the Random Oracle Model (ROM) is showing that proposed protocol is provably secure. The informal security analysis is also performed which demonstrates its robustness against all the threat assumptions in the Canetti-Krawczyk (C-K) model. Specifically, it is found to offer conditional privacy, unlinkability, anonymity, mutual authentication, session key establishment and perfect key secrecy. Moreover, it is capable enough to mitigate numerous threats such as forgery, impersonation, stolen verifier, replay, physical capture, de-synchronization, known secret key and smart card loss attacks. The performance is cost-effective as evident from the comparative analysis showing that the proposed protocol incurs the lowest energy consumptions and computation overheads at relatively low communication costs compared to many other authentication schemes. © 2014 IEEE.

关键词： Authentication elliptic curve cryptography Internet of Drones physically unclonable function security

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A Hybrid Bayesian-Genetic Approach for Micro Gear Tolerance Optimization

A Hybrid Bayesian-Genetic Approach for Micro Gear Tolerance ...

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International Conference on Automation and Computing (ICAC)

作者： Jin Jin Yuanping Xu Jia He Chaolong Zhang Zhijie Xu Chao Kong Benjun Guo Qiuyan Gai School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield Huddersfield UK School of Management Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

In precision engineering, particularly in microgear production, stringent tolerance levels are essential for ensuring optimal functionality. Traditional tolerance design models in this field focus primarily on product value, often overlooking aspects such as product robustness, which presents challenges in maintaining a balance between manufacturing precision and cost efficiency. This study proposes a hybrid optimization method that combines the predictive strengths of Bayesian techniques with the comprehensive search capabilities of genetic algorithms. The proposed methodology employed integrates statistical analysis with algorithmic modeling. Bayesian methods are utilized for forecasting and adjusting tolerance levels, using historical data and probabilistic models to enhance manufacturing accuracy. Concurrently, genetic algorithms are being applied to explore a range of design parameters, aiming to identify optimal tolerance settings. Combining these methods allows for an extensive exploration of potential solutions, seeking to achieve an equilibrium between precision and cost. Experimental results on a set of micro gear design cases demonstrate that the proposed BayesianGenetic Approach approach significantly outperforms the standalone Bayesian Optimization method and achieves competitive results compared to the standalone Genetic Algorithm method.

关键词： Costs Gears Statistical analysis Heuristic algorithms Optimization methods Robustness Bayes methods Manufacturing Genetic algorithms Convergence

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Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN

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computers, Materials & Continua 2021年第6期67卷 3505-3521页

作者： Maryam Shaq Humaira Ashraf Ata Ullah Mehedi Masud Muhammad Azeem N.Z.Jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia

The Internet of Things(IoT)is gaining attention because of its broad applicability,especially by integrating smart devices for massive communication during sensing ***-assisted Wireless Sensor Networks(WSN)are suitable for various applications like industrial monitoring,agriculture,and *** this regard,routing is challenging to nd an efcient path using smart devices for transmitting the packets towards big data repositories while ensuring efcient energy *** paper presents the Robust Cluster Based Routing Protocol(RCBRP)to identify the routing paths where less energy is consumed to enhances the network *** scheme is presented in six phases to explore ow and *** propose the two algorithms:(i)energy-efcient clustering and routing algorithm and (ii)distance and energy consumption calculation *** scheme consumes less energy and balances the load by clustering the smart *** work is validated through extensive simulation using *** elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption,the number of packets received at BS and the number of active and dead *** the future,we shall consider edge computing to analyze the performance of robust clustering.

关键词： Energy efciency routing load balancing cluster selection

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Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning

arXiv

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arXiv 2023年

作者： Zhang, Jinchuan Hui, Bei Mu, Chong Tian, Ling School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

Temporal Knowledge Graph (TKG) reasoning that forecasts future events based on historical snapshots distributed over timestamps is denoted as extrapolation and has gained significant attention. Owing to its extreme versatility and variation in spatial and temporal correlations, TKG reasoning presents a challenging task, demanding efficient capture of concurrent structures and evolutional interactions among facts. While existing methods have made strides in this direction, they still fall short of harnessing the diverse forms of intrinsic expressive semantics of TKGs, which encompass entity correlations across multiple timestamps and periodicity of temporal information. This limitation constrains their ability to thoroughly reflect historical dependencies and future trends. In response to these drawbacks, this paper proposes an innovative reasoning approach that focuses on Learning Multi-graph Structure (LMS). Concretely, it comprises three distinct modules concentrating on multiple aspects of graph structure knowledge within TKGs, including concurrent and evolutional patterns along timestamps, query-specific correlations across timestamps, and semantic dependencies of timestamps, which capture TKG features from various perspectives. Besides, LMS incorporates an adaptive gate for merging entity representations both along and across timestamps effectively. Moreover, it integrates timestamp semantics into graph attention calculations and time-aware decoders, in order to impose temporal constraints on events and narrow down prediction scopes with historical statistics. Extensive experimental results on five event-based benchmark datasets demonstrate that LMS outperforms state-of-the-art extrapolation models, indicating the superiority of modeling a multi-graph perspective for TKG reasoning. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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Examining the Role of Natural Language Processing in Generating Topics from Web Content

Examining the Role of Natural Language Processing in Generat...

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Computing (ICOCO), IEEE International Conference on

作者： Hamza H.M. Altarturi Muntadher Saadoon Nor Badrul Anuar Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia Department of Software Engineering Universiti Malaya Kuala Lumpur Malaysia

Natural Language Processing (NLP) is a major subfield of machine learning that focuses on the interaction between humans and computers through several techniques, including topic modeling, which extracts and generates topics from large amounts of unstructured data on the web. Topic modeling uncovers hidden patterns and contextual meanings in web pages, allowing for the categorization, clustering, recommendation of relevant content, detection of emerging trends, and improved information retrieval. The benefit of topic models relies on their performance to detect and generate topics; however, the evaluation of applying topic modeling on web content data remains lacking in current studies. The absence of examining such performance evaluations hinders understanding performance variability across different web pages, the development of refined models, and the ability to keep pace with emerging patterns in the dynamic online environment. This study presents a comprehensive performance evaluation of the benchmark topic models when applied to web content data. It presents a comparison of topic models using four topic coherence metrics, providing an in-depth performance analysis. The evaluation is conducted on a public dataset consisting of 2 million web pages, providing a substantial basis for assessing the strengths and limitations of these models in the context of web data. Additionally, this study examines the utilization of NLP techniques for topic generation within the context of web content and sheds light on the challenges and issues associated with generating topics from web content data, highlighting potential research prospects and the need for future work in the field of web topic modeling using natural language processing.

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Comparison on Visual Trackers: A Taxonomy 2

Comparison on Visual Trackers: A Taxonomy

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2nd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2022

作者： Bu, Yuqi Ge, Keyu Lei, Yitao School of Software Zhongyuan University of Technology Henan Zhengzhou China College of Overseas Education Nanjing Tech University Nanjing China School of Electrical and Computer Engineering Xiamen University Malaysia Selangor Sepang Malaysia

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Visual objective tracking is an important research problem in computer vision. Recent years have seen a significant increase in deep learning style correlation filtering due to developments in the field of deep learning and computer arithmetic power. The purpose of this paper is to present an overview of the approaches based on deep learning processing. We classify different articles through their network perspectives, and for each approach in the classification, including correlation filter, Siamese network, Multi-domain network. First introduce the problems handled by each approach, then analyze the process of the methods they use, and finally summarize their derived final results. After reading several papers, we conclude that these deep neural network-based approaches have improved the performance of visual tracking algorithms in recent years. In addition, we also analyze the future trends of visual target tracking and the problems that remain to be solved, and these works will help the future direction of development. © 2022 IEEE.

关键词： computer vision

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Detached and Interactive Multimodal Learning

arXiv

引用

arXiv 2024年

作者： Fan, Yunfeng Xu, Wenchao Wang, Haozhao Liu, Junhong Guo, Song Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Hubei China School of Software Beihang University Beijing China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data. However, traditional MML methods generally use the joint learning framework with a uniform learning objective that can lead to the modality competition issue, where feedback predominantly comes from certain modalities, limiting the full potential of others. In response to this challenge, this paper introduces DI-MML, a novel detached MML framework designed to learn complementary information across modalities under the premise of avoiding modality competition. Specifically, DI-MML addresses competition by separately training each modality encoder with isolated learning objectives. It further encourages cross-modal interaction via a shared classifier that defines a common feature space and employing a dimension-decoupled unidirectional contrastive (DUC) loss to facilitate modality-level knowledge transfer. Additionally, to account for varying reliability in sample pairs, we devise a certainty-aware logit weighting strategy to effectively leverage complementary information at the instance level during inference. Extensive experiments conducted on audio-visual, flow-image, and front-rear view datasets show the superior performance of our proposed method. The code is released at https://***/fanyunfeng-bit/DI-MML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Big-Moe: Bypassing Isolated Gating For Generalized Multimodal Face Anti-Spoofing

Big-Moe: Bypassing Isolated Gating For Generalized Multimoda...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yingjie Ma Zitong Yu Xun Lin Weicheng Xie Linlin Shen College of Computer Science and Software Engineering Shenzhen University Great Bay University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challenges due to modality biases and imbalances, as well as domain shifts. Our research introduces a Mixture of Experts (MoE) model to address these issues effectively. We identified three limitations in traditional MoE approaches to multimodal FAS: (1) Coarse-grained experts’ inability to capture nuanced spoofing indicators; (2) Gated networks’ susceptibility to input noise affecting decision-making; (3) MoE’s sensitivity to prompt tokens leading to overfitting with conventional learning methods. To mitigate these, we propose the Bypass Isolated Gating MoE (BIG-MoE) framework, featuring: (1) Fine-grained experts for enhanced detection of subtle spoofing cues; (2) An isolation gating mechanism to counteract input noise; (3) A novel differential convolutional prompt bypass enriching the gating network with critical local features, thereby improving perceptual capabilities. Extensive experiments on four benchmark datasets demonstrate significant generalization performance improvement in multimodal FAS task. The code is released at https://***/murInJ/BIG-MoE.

关键词： Convolutional codes Sensitivity Convolution Face recognition Noise Logic gates Feature extraction Security Speech processing Overfitting

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Automatic Instantiation of Assurance Cases from Patterns Using Large Language Models

arXiv

引用

arXiv 2024年

作者： Odu, Oluwafemi Belle, Alvine B. Wang, Song Kpodjedo, Segla Lethbridge, Timothy C. Hemmati, Hadi Lassonde School Of Engineering York University Toronto Canada School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Department of Software Engineering and Information Technology École de technologie supérieure Montreal Canada

An assurance case is a structured set of arguments supported by evidence, demonstrating that a system’s non-functional requirements (e.g., safety, security, reliability) have been correctly implemented. Assurance case patterns serve as templates derived from previous successful assurance cases, aimed at facilitating the creation of new assurance cases. Despite the use of these patterns to generate assurance cases, their instantiation remains a largely manual and error-prone process that heavily relies on domain expertise. Thus, exploring techniques to support their automatic instantiation becomes crucial. This study aims to investigate the potential of Large Language Models (LLMs) in automating the generation of assurance cases that comply with specific patterns. Specifically, we formalize assurance case patterns using predicate-based rules and then utilize LLMs, i.e., GPT-4o and GPT-4 Turbo, to automatically instantiate assurance cases from these formalized patterns. Our findings suggest that LLMs can generate assurance cases that comply with the given patterns. However, this study also highlights that LLMs may struggle with understanding some nuances related to pattern-specific relationships. While LLMs exhibit potential in the automatic generation of assurance cases, their capabilities still fall short compared to human experts. Therefore, a semi-automatic approach to instantiating assurance cases may be more practical at this time. Copyright © 2024, The Authors. All rights reserved.

关键词： Requirements engineering

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High-altitude Electromagnetic Pulse Parameter Identification and Experimental Verification

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Science technology and engineering 2025年第9期25卷 3749-3759页

作者： Qing, Chao-Jin Zhang, Yin-Jie Zhang, Min-Tao Wei, Mao-Gang Lin, Hui School of Electrical and Electronic Information Xihua University Chengdu610039 China School of Computer and Software Engineering Xihua University Chengdu610039 China Chengdu Aerospace Kate Electromechanical Technology Co. Ltd. Chengdu611730 China

In dynamic wireless environments, the distortion of transmission waveform is inevitably present, deteriorating the accuracy of identifying high altitude electromagnetic pulse (HEMP) parameters. To address this issue, an extreme learning machine parameter identification network (ELM-PInet)-based parameter identification method was investigated, which leverages the characteristics of HEMP waveform and considers the impact of wireless channels, thereby improving the accuracy of HEMP parameter identification. To demonstrate the nonlinear effects of wireless channels, the transmission model of HEMP waveform was first constructed based on wireless transmission theory. Subsequently, an ELM-PInet was developed to suppress waveform distortion and improve the identification accuracy of HEMP parameters. Finally, the proposed method was validated through field irradiation test on the experimental platform. Simulation results demonstrate that compared to classical HEMP parameter identification methods, the identification accuracy of HEMP parameters is enhanced by the proposed method. Furthermore, the ELM-PInet-based parameter identification method exhibits its robustness against the impacts of different parameters. Additionally, the effectiveness of the proposed method is further validated through field irradiation experiments. © 2025 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Electromagnetic pulse

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