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A Framework for Enhancing Privacy and Anonymity in Blockchain-Enabled IoT Devices

Computers, Materials & Continua 2024年第3期78卷 4263-4282页

作者： Muhammad Saad Muhammad Raheel Bhutta Jongik Kim Tae-Sun Chung Department of Artificial Intelligence Ajou UniversitySuwon443-749Korea Department of Electrical and Computer Engineering University of Utah Asia CampusIncheon21985Korea Department of Artificial Intelligence Chungnam National UniversityDaejeon34134Korea

With the increase in IoT(Internet of Things)devices comes an inherent challenge of *** the world today,privacy is the prime concern of every *** one’s privacy and keeping anonymity throughout the system is a desired functionality that does not come without inevitable trade-offs like scalability and increased complexity and is always exceedingly difficult to *** challenge is keeping confidentiality and continuing to make the person innominate throughout the *** address this,we present our proposed architecture where we manage IoT devices using blockchain *** proposed architecture works on and off blockchain integrated with the closed-circuit television(CCTV)security camera fixed at the rental *** this framework,the CCTV security camera feed is redirected towards the owner and renter based on the smart contract *** entity(owner or renter)can see the CCTV security camera feed at one *** is no third-party dependence except for the CCTV security camera deployment *** contributions include the proposition of framework architecture,a novel smart contract algorithm,and the modification to the ring signatures leveraging an existing cryptographic *** are made based on different systems’security and key management *** an empirical study,our proposed algorithm performed better in key generation,proof generation,and verification *** comparing similar existing schemes,we have shown the proposed architectures’*** now,we have developed this system for a specific area in the real ***,this system is scalable and applicable to other areas like healthcare monitoring systems,which is part of our future work.

关键词： Privacy anonymity blockchain IoT smart contracts

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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Computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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NOON-state interference in the frequency domain

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Light(Science & Applications) 2024年第5期13卷 843-851页

作者： Dongjin Lee Woncheol Shin Sebae Park Junyeop Kim Heedeuk Shin Department of Physics Pohang University of Science and Technology(POSTECH)Pohang 37673South Korea

The examination of entanglement across various degrees of freedom has been pivotal in augmenting our understanding of fundamental physics,extending to high dimensional quantum states,and promising the scalability of quantum *** this paper,we demonstrate the photon number path entanglement in the frequency domain by implementing a frequency beam splitter that converts the single-photon frequency to another with 50%probability using Bragg scattering four-wave *** two-photon NOON state in a single-mode fiber is generated in the frequency domain,manifesting the two-photon interference with two-fold enhanced resolution compared to that of single-photon interference,showing the outstanding stability of the *** successful translation of quantum states in the frequency domain will pave the way toward the discovery of fascinating quantum phenomena and scalable quantum information processing.

关键词： quantum state showing

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A Novel Framework to Construct S-Box Quantum Circuits Using System Modeling: Application to 4-Bit S-Boxes

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 545-561页

作者： Yongjin Jeon Seungjun Baek Jongsung Kim Department of Financial Information Security Kookmin UniversitySeoul02707Republic of Korea

Quantum computers accelerate many algorithms based on the superposition principle of quantum *** Grover algorithm provides significant performance to malicious users attacking symmetric key *** the performance of attacks using quantum computers depends on the efficiency of the quantum circuit of the encryption algorithms,research research on the implementation of quantum circuits is *** paper presents a new framework to construct quantum circuits of substitution boxes(S-boxes)using system *** model the quantum circuits of S-boxes using two layers:Toffoli and linear *** generate vector spaces based on the values of qubits used in the linear layers and apply them to find quantum *** framework finds the circuit bymatching elements of vector spaces generated fromthe input and output of a given S-box,using the forward search or themeet-in-the-middle *** developed a tool to apply this framework to 4-bit *** the 4-bit S-box quantum circuit construction tool LIGHTER-R only finds circuits that can be implemented with four qubits,the proposed tool achieves the circuits with five *** proposed tool can find quantum circuits of 4-bit odd permutations based on the controlled NOT,NOT,and Toffoli gates,whereas LIGHTER-R is unable to perform this task in the same *** expect this technique to become a critical step toward optimizing S-box quantum circuits.

关键词： System modeling quantum circuit S-box circuit quantum computer

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Reconfigurable transmissive metasurface with a combination of scissor and rotation actuators for independently controlling beam scanning and polarization conversion

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Microsystems & Nanoengineering 2024年第2期10卷 173-180页

作者： Chhunheng Lor Ratanak Phon Sungjoon Lim Intelligent Semiconductor Engineering Chung-Ang UniversityHeukseok-DongDongjak-GuSeoul06974Republic of Korea School of Electrical and Electronic Engineering Chung-Ang UniversityHeukseok-DongDongjak-GuSeoul06974Republic of Korea

Polarization conversion and beam scanning metasurfaces are commonly used to reduce polarization mismatch and direct electromagnetic waves in a specific direction to improve the strength of a wireless ***,identifying suitable active and mechanically reconfigurable metasurfaces for polarization conversion and beam scanning is a considerable challenge,and the reported metasurfaces have narrow scanning ranges,are expensive,and cannot be independently *** this paper,we propose a reconfigurable transmissive metasurface combined with a scissor and rotation actuator for independently controlling beam scanning and polarization conversion *** metasurface is constructed with rotatable unit cells(UCs)that can switch the polarization state between right-handed(RHCP)and left-handed circular polarization(LHCP)by flipping the UCs to reverse their phase ***,independent beam scanning is achieved using the scissor actuator to linearly change the distance between the *** and experimental results confirm that the proposed metasurface can perform beam scanning in the range of 28°for both the positive and negative regions of a radiation pattern(RHCP and LHCP beams)at an operational frequency of 10.5 GHz.

关键词： beam rotation controlling

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Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

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Journal of Bionic Engineering 2024年第1期21卷 447-485页

作者： Sanjay Bhardwaj Da-Hye Kim Dong-Seong Kim Department of IT Convergence Engineering ICT Convergence Research CenterKumoh National Institute of TechnologyGumiSouth Korea

In industrial wireless networks,data transmitted from source to destination are highly *** often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet ***,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless *** term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for *** a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base *** includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue *** features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue *** analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and *** results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC ***,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ***,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other *** results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet *** proposed a

关键词： Dragonfly Damselfly Interaction Queuing delay Optimization Industrial wireless networks

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Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1669-1687页

作者： So-Eun Jeon Ye-Sol Oh Yeon-Ji Lee Il-Gu Lee Department of Future Convergence Technology Engineering Sungshin Women’s UniversitySeoul02844Korea Department of Convergence Security Engineering SungshinWomen’s UniversitySeoul02844Korea

With the advancement of wireless network technology,vast amounts of traffic have been generated,and malicious traffic attacks that threaten the network environment are becoming increasingly *** signature-based detection methods,static analysis,and dynamic analysis techniques have been previously explored for malicious traffic detection,they have limitations in identifying diversified malware traffic *** research has been focused on the application of machine learning to detect these ***,applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning *** this study,we examined methods for effectively utilizing machine learning-based malicious traffic detection approaches for lightweight *** introduced the suboptimal feature selection model(SFSM),a feature selection technique designed to reduce complexity while maintaining the effectiveness of malicious traffic *** performance was evaluated on various malicious traffic,benign,exploits,and generic,using the UNSW-NB15 dataset and SFSM sub-optimized hyperparameters for feature selection and narrowed the search scope to encompass all *** improved learning performance while minimizing complexity by considering feature selection and exhaustive search as two steps,a problem not considered in conventional *** experimental results showed that the detection accuracy was improved by approximately 20%compared to the random model,and the reduction in accuracy compared to the greedy model,which performs an exhaustive search on all features,was kept within 6%.Additionally,latency and complexity were reduced by approximately 96%and 99.78%,respectively,compared to the greedy *** study demonstrates that malicious traffic can be effectively detected even in lightweight device *** verified the possibility of detecting various attack

关键词： Feature selection lightweight device machine learning Internet of Things malicious traffic

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Design-space adaptation method for multiobjective and multidisciplinary optimization

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Chinese Journal of Aeronautics 2024年第8期37卷 166-189页

作者： Jongho JUNG Kwanjung YEE Shinkyu JEONG Department of Aerospace Engineering Seoul National UniversitySeoul 08826Republic of Korea Department of Mechanical Engineering Kyunghee UniversityYoungin 17104Republic of Korea

This paper developed a new method that adaptively adjusts a design space by considering the actual solution distribution of a problem to overcome the conventional design-space adaptation method that assumes the solutions distribution to be a normal distribution because the distributions of solutions are rarely normal distributions for real-world *** developed method was applied to nineteen multiobjective test functions that are widely used to evaluate the characteristics and performance of optimization *** results showed that this method adapted the design space to an appropriate design space where the solution existence probability was *** optimization performance achieved using the developed method was higher than that of the conventional ***,the developed method was applied to the conceptual design of an unmanned spacecraft to confirm its validity in real-world design and multidisciplinaryoptimization *** results showed that the Pareto solutions of the developed method were superior to those of conventional ***,the optimization efficiency with the developed method was improved by more than 1.4 times over that of the conventional *** this regard,the developed method has the potential to be applied to complicated real-world optimization problems to achieve better performance and efficiency.

关键词： Multiobjective optimization Multiobjective genetic algorithm Design-space adaptation Multidisciplinary optimization Hypersonic vehicle

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Real-Time Object Detection and Face Recognition Application for the Visually Impaired

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Computers, Materials & Continua 2024年第6期79卷 3569-3583页

作者： Karshiev Sanjar Soyoun Bang SookheeRyue Heechul Jung Department of Artificial Intelligence Kyungpook National UniversityDaegu41466Republic of Korea Haga Co. Ltd.Daegu38428Republic of Korea

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable *** approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall ***,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing *** study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application *** introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,*** results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their ***,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition *** comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

关键词： Artificial intelligence deep learning real-time object detection application

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Integrating Ontology-Based Approaches with Deep LearningModels for Fine-Grained Sentiment Analysis

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Computers, Materials and Continua 2024年第1期81卷 1855-1877页

作者： Zhao, Longgang Lee, Seok-Won The Knowledge-Intensive Software Engineering (NiSE) Research Group Department of Artificial Intelligence Ajou University Suwon City 16499 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon City 16499 Korea Republic of

Although sentiment analysis is pivotal to understanding user preferences, existing models face significant challenges in handling context-dependent sentiments, sarcasm, and nuanced emotions. This study addresses these challenges by integrating ontology-based methods with deep learning models, thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback. The framework comprises explicit topic recognition, followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis. In the context of sentiment analysis, we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding. Furthermore, we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons. We evaluate the performance of our system using a dataset of 10,500 restaurant reviews, focusing on sentiment classification accuracy. The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions, thereby improving the overall sentiment-analysis performance. Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy. © 2024 The Authors. Published by Tech Science Press.

关键词： Contrastive Learning

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