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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

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computers, Materials & Continua 2025年第3期82卷 4181-4218页

作者： Haydar Abdulameer Marhoon Rafid Sagban Atheer Y.Oudah Saadaldeen Rashid Ahmed Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-Qar64011Iraq College of Computer Sciences and Information Technology University of KerbalaKarbala56001Iraq Enginerring Technical College Al-Ayen UniversityThi-Qar64011Iraq Information Technology College University of BabylonHilla51002Iraq Department of Computer Science College of Education for Pure ScienceUniversity ofThi-QarNasiriyah64001Iraq Artificial Intelligence Engineering Department College of EngineeringAl-Ayen UniversityThi-Qar64001Iraq Department of Computer Science Bayan UniversityErbilKurdistan44001Iraq

In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational *** proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic *** approach not only improves the *** are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning *** are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble *** implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline *** train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor *** used Monte Carlo simulations to extract these *** on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.

关键词： Intrusion detection system(IDS) hybrid models of CNN-LSTM WSN extreme gradient boosting(XGBoost)regressor ensemble model

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Efficiency Evaluation of a Combining Compact 1D CNN and Signal Filtering Prototype Algorithm for Vibration Signal Classification 4

Efficiency Evaluation of a Combining Compact 1D CNN and Sign...

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4th International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Krinsukont, Thosapon Sirithanapipat, Taweedej National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Kasetsart University Department of Mechanical Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents an experiment and results of the modified CNN algorithm, it was developed by combining a compact 1D convolution neural network with a tuned signal filter (low-pass filter in this experiment). The algorithm was specifically designed for signal shape detection, prediction, and classification. An experiment aimed to evaluate the created algorithm's structure and signal filter to ensure its suitability for vibration signal data analysis. A testbed was built using an inverter motor equipped with various unbalanced conditions by increasing screws. Four-class unbalanced conditions were studied compared with the normal condition. Each conditions produced the distinct signal shape, with slight differences, making them suitable for evaluating the proposed algorithm's performance. The results demonstrate the satisfied performance of the algorithm, which effectively classifies vibration signals unbalance-3 (precision 54%), unbalance-4 (precision 77%) and a group of normal, unbalance-1, and unbalance-2. The group of normal, unbalance-1, and unbalance-2 can't be easily classified due to the tiny differences in the signal shapes, caused by small mass variations of the unbalanced screw unbalance-1, and unbalance-2. In conclusion, the algorithm proved satisfied effectiveness for its intended purpose of vibration signal classification. © 2025 IEEE.

关键词： Testbeds

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Robust intrusion detection for network communication on the Internet of Things: a hybrid machine learning approach

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Cluster Computing 2024年第7期27卷 9975-9991页

作者： Soltani, Nasim Rahmani, Amir Masoud Bohlouli, Mahdi Hosseinzadeh, Mehdi Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliu Taiwan Research and Innovation Department Petanux GmbH Bonn Germany Institute of Research and Development Duy Tan University Da Nang Viet Nam Department of Computer Science University of Human Development Sulaymaniyah Iraq

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Predicting Gold Prices with Rolling Average Representation and Machine Learning 1

Predicting Gold Prices with Rolling Average Representation a...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Kannan, Rajkumar Andres, Frederic Muthu, Kalaiarasi Sonai Anutariya, Chutiporn Department of Computer Science Tiruchirappalli India National Institute of Informtics Digital Content and Media Science Tokyo Japan Multimedia University Faculty of Information Science and Technology Malaysia Asian Institute of Technology School of Engineering and Technology Pathum Thani Thailand

ISBN: (纸本)9798350348637

Price prediction and forecasting have been important topics that have been long studied in the financial world. Commodity price prediction is one such highly popular application in stock markets across the world. Among all commodities such as gold, silver, platinum and others, gold has continued to be the prime and forefront commodity which decides the price movements of other commodities. Moving average approach and its variants are the important technical indicators which help financial institutions, investors and traders to understand the movement of gold prices throughout the year. Some of the popular technical indicators are simple moving averages, cumulative moving averages and exponential moving averages. This paper proposes a unique gold price prediction methodology by building popular ensemble machine learning models utilizing all three types of moving average values as feature representation and next day price as the target for prediction. The predicted values based on the three moving average features are presented to users, investors and traders, so that investors and traders will know the gold price for the next day. Extensive experiments are conducted on the Indian gold price daily data from December 2011 to March 2024 by comparing six prominent machine learning models and ARIMA model. We also present a detailed study of different feature importance. Experimental results on the gold price daily data have confirmed that polynomial regression models have given superior performance in terms of regression accuracy and error metrics namely MSE, RMSE and MAE © 2024 IEEE.

关键词： Prediction models

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Advancing general robotic manipulation with multimodal foundation models: An embodied AI paradigm

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science China Technological sciences 2025年第5期68卷 294-296页

作者： Shifeng HUANG He WANG Xing ZHOU Wenkai CHEN Haibin YANG Jianwei ZHANG Department of Mechanical and Aerospace Engineering Hong Kong University of Science and Technology Foshan Institute of Intelligent Equipment Technology Foshan Huashu Robot Co. Ltd. Center on Frontiers of Computing Studies (CFCS) School of Computer SciencePeking University Beijing Academy of Artificial Intelligence Department of Informatics Universit?t Hamburg

Can current robotic technologies truly replicate the full scope and intricacies of human labour? In practice, the adoption of robots remains limited, especially in open, unstructured environments commonly encountered in everyday scenarios such as services, healthcare, agriculture,construction, and numerous other fields. From the perspective of general robotic manipulation, the challenges arise from three factors.

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A Zero-Watermark Scheme Based on Quaternion Generalized Fourier Descriptor for Multiple Images

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computers, Materials & Continua 2022年第5期71卷 2633-2652页

作者： Baowei Wang Weishen Wang Peng Zhao Naixue Xiong School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China Department of Mathematics and Computer Science Northeastern State UniversityTahlequahOK74464USA

Most of the existing zero-watermark schemes for medical images are only appropriate for a single grayscale *** they protect a large number of medical images,repeating operations will cause a significant amount of time and storage ***,this paper proposes an efficient zero-watermark scheme for multiple color medical images based on quaternion generalized Fourier descriptor(QGFD).Firstly,QGFD is utilized to compute the feature invariants of each color image,then the representative features of each image are selected,stacked,and reshaped to generate a feature matrix,which is then binarized to get a binary feature *** information can be converted into the copyright image by using QR code technology,which contains more ***,the zero-watermark image is constructed by executing the XOR operation on the copyright image and the feature image scrambled by the Cat *** the experiment,different parameters are selected to determine the maximum number of images that the proposed scheme can protect simultaneously while achieving good *** experimental results demonstrate that the proposed scheme can effectively resist common attacks,geometric attacks and joint attacks,and effectively improve the operation efficiency of the algorithm,thus effectively decreasing the time and storage cost of copyright protection for lots of medical images.

关键词： Copyright protection color medical image zero-watermark QGFD

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