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Adaptive Emulation Framework for Multi-Architecture IoT Firmware Testing

computers, Materials & Continua 2023年第5期75卷 3291-3315页

作者： Jihyeon Yu Juhwan Kim Youngwoo Lee Fayozbek Rustamov Joobeom Yun Department of Computer and Information Security and Convergence Engineering for Intelligent DroneSejong UniversitySeoul05006Korea Department of Computer and Information Security Sejong UniversitySeoul05006Korea

Internet of things(IoT)devices are being increasingly used in numerous ***,the low priority on security and various IoT types have made these devices vulnerable to *** prevent this,recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated ***,these studies focused only on major firmware architectures and rarely considered exotic *** addition,because of the diversity of firmware,the emulation success rate is not high in terms of large-scale *** this study,we propose the adaptive emulation framework for multi-architecture(AEMA).In the field of automated emulation frameworks for IoT firmware testing,AEMA considers the following issues:(1)limited compatibility for exotic firmware architectures,(2)emulation instability when configuring an automated environment,and(3)shallow testing range resulting from structured *** tackle these problems,AEMAcan emulate not onlymajor firmware architectures but also exotic firmware architectures not previously considered,such as Xtensa,ColdFire,and reduced instruction set computer(RISC)version five,by implementing a minority ***,we applied the emulation arbitration technique and input keyword extraction technique for emulation stability and efficient test case *** compared AEMA with other existing frameworks in terms of emulation success rates and fuzz *** a result,AEMA succeeded in emulating 864 out of 1,083 overall experimental firmware and detected vulnerabilities at least twice as fast as the experimental ***,AEMAfound a 0-day vulnerability in realworld IoT devices within 24 h.

关键词： Internet of things(IoT) emulation framework firmware fuzzing concolic execution vulnerability

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Reconfigurable assembly of self-healing stretchable transistors and circuits for integrated systems

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Nature Electronics 2025年 1-11页

作者： Jang, Jaepyo Choo, Hyongsuk Lee, Sangkyu Song, Jihyang Park, Kyuha Yoon, Jiyong Seong, Duhwan An, Soojung Jung, Hyunjin Ju, Jaewon Kang, Juncheol Kang, Joohoon Kim, In Soo Shin, Mikyung Park, Jin-Hong Son, Donghee Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Center for Neuroscience Imaging Research Institute for Basic Science (IBS) Suwon South Korea Department of Artificial Intelligence System Engineering Sungkyunkwan University Suwon South Korea Department of Intelligent Precision Healthcare Convergence Sungkyunkwan University (SKKU) Suwon South Korea School of Advanced Materials Science and Engineering Sungkyunkwan University (SKKU) Suwon South Korea KIST-SKKU Carbon-Neutral Research Center Sungkyunkwan University (SKKU) Suwon South Korea Nanophotonics Research Center Korea Institute of Science and Technology Seoul South Korea Department of Biomedical Engineering Sungkyunkwan University (SKKU) Suwon South Korea SKKU Advanced Institute of Nanotechnology (SAINT) Sungkyunkwan University Suwon South Korea Department of Semiconductor Convergence Engineering Sungkyunkwan University (SKKU) Suwon South Korea

Self-healing soft electronic devices that can recover their mechanical and electrical properties are of use in the development of long-term wearable and implantable electronic systems. However, creating self-healing and stretchable integrated circuits is challenging due to the absence of suitable materials and sufficiently customizable assembly technology. Here we report a reconfigurable and scalable assembly method for self-healing, stretchable, active-type devices, including thin-film transistors, active-matrix arrays and logic gates. The self-healing, stretchable, thin-film transistor can easily be fabricated by transfer-printing of intrinsically soft constituent films: an insulating self-healing polymer for the gate dielectric, a semiconducting nanocomposite for the active channel and a carbon-nanotube-embedded composite for the electrodes. Our assembly method allows the thin-film transistors to be extended to wearable and implantable 5 × 5 active-matrix, soft and self-healing transistor arrays. These arrays can multiplex pressure data recorded from a 5 × 5 tactile sensor array, provide feedback control to an array of soft and self-healing optoelectronic pixels, and maintain electrical performance even when implanted in the subcutaneous tissue of a rodent model. To demonstrate user-on-demand functionality, we combined, disassembled and recombined thin-film transistors and load resistors into three different types of logic gates (inverter, NAND and NOR circuits). © The Author(s), under exclusive licence to Springer Nature Limited 2025.

关键词： NAND circuits

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Visual Analytics System of Comprehensive Data Quality Improvement for Machine Learning using Data- and Process-driven Strategies

Visual Analytics System of Comprehensive Data Quality Improv...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Hong, Hyein Yoo, Sangbong Jin, Yejin Yoon, Chanyoung Yim, Soobin Choi, Seokhwan Jang, Yun Sejong University Department of Computer Engineering and Convergence Engineering for Intelligent Drone Seoul Korea Republic of Sejong University Department of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9781665480451

Machine learning (ML) models are used to mine inconspicuous information in big data. The model and data quality influence the performance of a ML model. However, modifying the ML model while measuring performance is impractical, and low-quality data causes biased model training. Therefore, improving the data quality is essential. Visual analytics systems supporting DQI (Data Quality Improvement) have been proposed in the past. However, in the studies, it is difficult for users to assess comprehensive data quality improvement methods for machine learning and to determine an appropriate data quality improvement process. In this paper, we propose a novel visual analytics system for managing data quality used in machine learning models. © 2022 IEEE.

关键词： Machine learning

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MV-Honeypot:Security Threat Analysis by Deploying Avatar as a Honeypot in COTS Metaverse Platforms

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computer Modeling in engineering & sciences 2024年第10期141卷 655-669页

作者： Arpita Dinesh Sarang Mohsen Ali Alawami Ki-Woong Park SysCore Lab.(Convergence Engineering for Intelligent Drone) Sejong UniversitySeoul05006Republic of Korea Division of Computer Engineering Hankuk University of Foreign StudiesYongin-si17035Republic of Korea Department of Information Security Sejong UniversitySeoul05006Republic of Korea

Nowadays,theuse of Avatars that are unique digital depictions has increased by users to access Metaverse—a virtual reality environment—through multiple devices and for various ***,the Avatar and Metaverse are being developed with a new theory,application,and design,necessitating the association of more personal data and devices of targeted users every *** Avatar and Metaverse technology explosion raises privacy and security concerns,leading to cyber ***-Honeypot,or Metaverse-Honeypot,as a commercial off-the-shelf solution that can counter these cyber attack-causing vulnerabilities,should be *** fill this gap,we study user’s engagements with Avatars in Metaverse,analyze possible security vulnerabilities,and create a model named Simplified Avatar Relationship Association with Non-linear Gradient(SARANG)that draws the full diagram of infrastructure components and data flow through accessing Metaverse in this *** also determine the most significant threat for each component’s cyberattacks that will affect user data and *** a result,the commercial off-the-shelf(COTS)of the MV-Honeypot must be established.

关键词： Avatar metaverse cybersecurity cloud computing internet of things artificial intelligence security analysis

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engineering cobalt nickel oxide nanowires embedded in tungsten disulfide/reduced graphene oxide hybrid composites for supercapacitor applications and overall water-splitting reactions

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Journal of Colloid and Interface science 2025年 697卷 137965页

作者： Sajjad Hussain Dhanasekaran Vikraman Zeesham Abbas Zulfqar Ali Sheikh Sikandar Aftab Iftikhar Hussain Shoyebmohamad F. Shaikh Hyun-Seok Kim Deok-Kee Kim Jongwan Jung Hybrid Materials Center (HMC) Sejong University Seoul 05006 Republic of Korea Department of Nanotechnology and Advanced Materials Engineering Sejong University Seoul 05006 Republic of Korea Division of Electronics and Electrical Engineering Dongguk University-Seoul Seoul 04620 Republic of Korea Department of Electrical Engineering and Department of Convergence Engineering for Intelligent Drone Sejong University Seoul 05006 Republic of Korea Department of Intelligent Mechatronics Engineering Sejong University Seoul 05006 Republic of Korea Department of Mechanical Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong Department of Chemistry College of Science King Saud University P.O. Box 2455 Riyadh 11451 Saudi Arabia

This paper presents the fabrication of hierarchical hollow 3D nanowires-like cobalt nickel oxide nanowires (NWs) embedded in tungsten disulfide/reduced graphene oxide hybrid (CoNiO 2 @WS 2 /rGO) composite through a facile hydrothermal process. The interaction between the 3D hollow WS 2 /rGO skeleton network and the well-defined CoNiO 2 NWs enabled the remarkable electrochemical supercapacitor performances constructed with an enriched specific capacity (515C/g at 0.5 A/g) and superior cycling solidity (97.5 %). Asymmetric device assembled engaging the CoNiO 2 @WS 2 /rGO composite displayed a 236F/g specific capacitance at 1 A/g with ∼74 Wh/kg energy density at 2.4 kW/kg power density along with a high cycling stability (95.2 %). Furthermore, CoNiO 2 @WS 2 /rGO composite possessed bundles of pores with strong interfacial connection, and this enabled a large accessible surface area on the nanowires and facilitated the release of gas bubbles, resulting in excellent oxygen evolution and hydrogen evolution kinetics with a small overpotential ( η 10 = 195 and 33 mV, respectively). Assembled CoNiO 2 @WS 2 /rGO (+/-) electrolyzer achieved a current density of 10 mA cm −2 at a minimal cell voltage of 1.43 with long-span strength. Additionally, theoretical computation studies confirmed that the exceptional catalytic efficacy of the fabricated catalyst could be attributed to the transfer of charge from WS 2 /rGO to CONiO 2 NWs.

关键词： CoNiO(2) Supercapacitors WS(2) Water splitting rGO

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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computer Systems science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Provably secure signature-based anonymous user authentication protocol in an Internet of Things-enabled intelligent precision agricultural environment

Provably secure signature-based anonymous user authenticatio...

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作者： Vangala, Anusha Das, Ashok Kumar Lee, Jong-Hyouk Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of

User authentication is a promising security solution in which an external user having his/her mobile device can securely access the real-time information directly from the deployed smart devices in an Internet of Things-enabled intelligent precision agricultural environment. To achieve this goal, we present a new signature-based three factor user authentication scheme. The established session key between a user and the accessed smart device is then used to make secure communication among them to fetch the real-time data of the device. A detailed security analysis including the random-oracle based formal security, formal security verification using the broadly recognized automated validation of internet security protocols and applications tool and nonmathematical informal security analysis show the robustness of the proposed scheme against a number of potential attacks. In addition, testbed experiments are performed for measuring computational time of various cryptographic primitives that are used for comparative study among the proposed scheme and other related existing competing schemes. The detailed comparative analysis shows that the proposed scheme has a better trade-off among its offered security and functionality features, and communication and computational overheads as compared with those for other competing schemes. © 2021 John Wiley & Sons, Ltd.

关键词： Internet of things

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Decentralized identifier based illegal digital content distribution tracking system using the format-preserving encryption algorithm

Decentralized identifier based illegal digital content distr...

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作者： Kwon, Soonhong Park, Jaehyoung Lee, Taeyang Oh, SungHeun Lee, Jong-Hyouk Department of Computer and Information Security & Convergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of Protocol Engineering Lab. Sejong University Seoul Korea Republic of Technical Research Center DigiCAP Seoul Korea Republic of

The coronavirus pandemic, which first began in December 2019, has completely changed our daily lives. In particular, daily online digital content consumption over the world has soared since the start of the coronavirus pandemic. For preventing illegal digital content distribution, various technologies have been applied, but current copyright protection technologies are still lacking to provide secure digital content distribution while preserving privacy. In this article, we introduce a new digital content distribution system, which utilizes blockchain-based decentralized identifier and format-preserving encryption to enable user privacy preserved digital content tracking. We present the system architecture with detailed message flows of the proposal and provide its analysis result in terms of security and overhead. © 2022 John Wiley & Sons Ltd.

关键词： Coronavirus

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Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model

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computer Systems science & engineering 2023年第11期47卷 1633-1650页

作者： T.Satyanarayana Murthy Kollati Vijaya Kumar Fayadh Alenezi E.Laxmi Lydia Gi-Cheon Park Hyoung-Kyu Song Gyanendra Prasad Joshi Hyeonjoon Moon Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of International Affairs and Education Gangseo UniversitySeoul07661Korea Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from ***,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing ***,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human *** automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human ***’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several *** the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)*** the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the *** addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature *** fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of *** performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit *** comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.

关键词： Fruit classification computer vision machine learning deep learning metaheuristics

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A novel feature fusion and mountain gazelle optimizer based framework for the recognition of jute pests in sustainable agriculture

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Scientific reports 2025年第1期15卷 18148页

作者： Soner Kiziloluk Mucahit Karaduman Serpil Aslan Muhammed Yildirim Muhammad Attique Khan Fatimah Alhayan Yunyoung Nam Department of Computer Engineering Faculty of Engineering and Natural Sciences Malatya Turgut Ozal University Malatya Turkey. Department of Software Engineering Faculty of Engineering and Natural Sciences Malatya Turgut Ozal University Malatya Turkey. Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia. attique.khan@***. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O.Box 84428 11671 Riyadh Saudi Arabia. Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang University Asan 31538 Korea. ynam@sch.ac.kr.

Sustainable agriculture is an approach that involves adopting and developing agricultural practices to increase efficiency and preserve resources, both environmentally and economically. Jute is one of the primary sources of income grown in many countries. At this stage, increasing efficiency in jute production and protecting it from pests is essential. Detecting jute pests at an early stage will not only improve crop yield but also provide more income. In this paper, an artificial intelligence-based model was suggested to detect jute pests at an early stage. In this developed model, two different pre-trained models were used for feature extraction. To improve the performance of the developed model, the features obtained using the DarkNet-53 and DenseNet-201 models were combined. After this stage, the metaheuristic Mountain Gazelle Optimizer (MGO) was used, allowing the developed model to work faster and achieve more successful results. Feature selection was carried out using MGO; thus, more successful results were obtained with fewer, more compelling features. The proposed model was compared with six different models and five different classifiers accepted in the literature. In the developed model, 17 different jute pests were detected with 96.779% accuracy. The accuracy value achieved in the developed model is promising in successfully detecting jute pests.

关键词： Classification Deep learning Jute Mountain gazelle optimizer Optimization

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