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Machine learning for Internet of Things (IoT) device identification: a comparative study

PeerJ Computer Science 2025年 11卷 1-34页

作者： Tahaei, Hamid Liu, Anqi Forooghikian, Hamid Gheisari, Mehdi Zaki, Faiz Anuar, Nor Badrul Fang, Zhaoxi Huang, Longjun Institute of Artificial Intelligence Shaoxing University Shaoxing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Management Islamic Azad University of Kish Kish Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Selangor Kuala Lumpur Malaysia Department of Computer Science and Engineering Shaoxing University Shaoxing China

The rapid deployment of millions of connected devices brings significant security challenges to the Internet of Things (IoT). IoT devices are typically resource-constrained and designed for specific tasks, from which new security challenges are introduced. As such, IoT device identification has garnered substantial attention and is regarded as an initial layer of cybersecurity. One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. Yet, the domain needs a comparative analysis of the accuracy of different classifiers and FS algorithms to comprehend their true capabilities in various datasets. This article provides a comprehensive performance evaluation of several reputable classifiers being used in the literature. The study evaluates the efficacy of filter-and wrapper-based FS methods across various ML classifiers. Additionally, we implemented a Binary Green Wolf Optimizer (BGWO) and compared its performance with that of traditional ML classifiers to assess the potential of this binary meta-heuristic algorithm. To ensure the robustness of our findings, we evaluated the effectiveness of each classifier and FS method using two widely utilized datasets. Our experiments demonstrated that BGWO effectively reduced the feature set by 85.11% and 73.33% for datasets 1 and 2, respectively, while achieving classification accuracies of 98.51% and 99.8%, respectively. The findings of this study highlight the strong capabilities of BGWO in reducing both the feature dimensionality and accuracy gained through classification. Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets. 2025 Tahaei et al.

关键词： Cyber attacks

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Normalized Least Dependent Difference: A Method in Solving Incorrectly Identified Monotonic Linearity Relationship in Linear Regression

Normalized Least Dependent Difference: A Method in Solving I...

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2025 International Workshop on Advanced Imaging technology, IWAIT 2025

作者： Yeap, Herrick Han Lin Lim, Le Ying Lim, Michelle Chee Ern Chow, Brandon Chen Hong Ong, Hui Yan Kimberly Eu, Kok Seng Yap, Kian Meng Research Centre for Human-Machine Collaboration Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Petaling Jaya47500 Malaysia School of Engineering Faculty of Innovation & Technology Taylor's University Subang Jaya47500 Malaysia

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

In machine learning, the reliability of an analytical method is crucial in validating the assumption of linearity for any linear regression *** existing analytical method of proving the assumption linearity, such as Pearson's Correlation Coefficient (PCC), Spearman's Rank Correlation Coefficient (SRCC) and Kendall's Tau Correlation Coefficient (KTCC), has its limitation as it does not work in monotonic relationship *** this paper, we propose a Normalized Least Dependent Difference (NLDD) method to improve the limitation of existing linearity method in identifying monotonic relationship *** calculating the difference between each data point and its predicted value on the regression line, we can determine how much the predicted value deviates from the observed value.A consistent difference between each data point and its predicted value, represented by a relative standard deviation in the y-axis that is near to its mean, suggests that the model accurately reflects the relationship between the dependent variable and the independent *** findings show that our NLDD is effective in identifying linearity in linear relationship graph and non-linearity in monotonic relationship graph. © 2024 SPIE.

关键词： Multiple linear regression

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Intrusion Detection for Internet of Medical Things (IoMT) using Extreme Learning Machine

Intrusion Detection for Internet of Medical Things (IoMT) us...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Asma Cherif Information Technology Department Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

The proliferation of Internet of Medical Things (IoMT) devices in healthcare requires robust intrusion detection systems to safeguard sensitive data and ensure patient safety. This study presents a novel approach utilizing Extreme Learning Machine (ELM) architectures for intrusion detection in IoMT environments, leveraging the WUSTL-EHMS-2020 dataset. Addressing the prevalent issue of class imbalance, we compare the impact of Synthetic Minority Over-sampling Technique (SMOTE), weighted loss functions, and a hybrid over- and under-sampling strategy to enhance detection accuracy. Our research implements and evaluates multiple ELM architectures, comparing their performance in terms of accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). The results demonstrate that the weighted loss function method significantly improves the detection capabilities of ELMs, providing an effective solution for real-time intrusion detection in healthcare IoMT systems.

关键词： Technological innovation Accuracy Machine learning algorithms Extreme learning machines Smart cities Intrusion detection Internet of Medical Things Real-time systems Safety Security

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TOWARDS THE DEVELOPMENT OF BALANCED SYNTHETIC DATA FOR CORRECTING GRAMMATICAL ERRORS IN ARABIC: AN APPROACH BASED ON ERROR TAGGING MODEL AND SYNTHETIC DATA GENERATING MODEL

arXiv

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arXiv 2025年

作者： Alrehili, Ahlam Alhothali, Areej Department of Computer Sciences Faculty of Computing and Information Technology King Abdulaziz University Saudi Electronic University Medina Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Synthetic data generation is widely recognized as a way to enhance the quality of neural grammatical error correction (GEC) systems. However, current approaches often lack diversity or are too simplistic to generate the wide range of grammatical errors made by humans, especially for low-resource languages such as Arabic. In this paper, we will develop the error tagging model and the synthetic data generation model to create a large synthetic dataset in Arabic for grammatical error correction. In the error tagging model, the correct sentence is categorized into multiple error types by using the DeBERTav3 model. Arabic Error Type Annotation tool (ARETA) is used to guide multi-label classification tasks in an error tagging model in which each sentence is classified into 26 error tags. The synthetic data generation model is a back-translation-based model that generates incorrect sentences by appending error tags before the correct sentence that was generated from the error tagging model using the ARAT5 model. In the QALB-14 and QALB-15 Test sets, the error tagging model achieved 94.42% F1, which is state-of-the-art in identifying error tags in clean sentences. As a result of our syntactic data training in grammatical error correction, we achieved a new state-of-the-art result of F1-Score: 79.36% in the QALB-14 Test set. We generate 30,219,310 synthetic sentence pairs by using a synthetic data generation model. Our data is accessible to the public1 © 2025, CC BY.

关键词： Data assimilation

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks 5

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Lasantha, N W Chanaka Maduranga, M.W.P. Abeysekara, Ruvan Tilwari, Valmik Chakraborty, Nilotpal Sharma, Debashree Iic University of Technology Faculty of Graduate Studies Phnom Penh Cambodia University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Nugegoda Sri Lanka Bcas Campus Faculty of Computing Colombo Sri Lanka Indian Institute of Information Technology Department of Computer Science Engineering Guwahati India Indian Institute of Information Technology Department of Electronics & Communication Engineering Guwahati India

ISBN: (纸本)9798331530983

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands. © 2025 IEEE.

关键词： Cyber attacks

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (Science and information Organization). All rights reserved.

关键词： Rain

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Ant Colony Optimization for Multi-Label Correlation-based Feature Selection Method 10

Ant Colony Optimization for Multi-Label Correlation-based Fe...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Jungjit, Suwimol Prasitsupparote, Amonrat Nathan, Shelena Soosay Rattanakietkhajorn, Thanawit Thaksin University Faculty of Science and Digital Innovation Phatthalung Thailand Prince of Songkla University College of Computing Phuket Thailand Universiti Tun Hussein Onn Center for Diploma Studies Dept of Information Technology Johor Malaysia Suratthani Rajabhat University Faculty of Science and Technology Suratthani Thailand

ISBN: (纸本)9798331543273

This paper proposes a new ant colony optimization for Multi-Label Correlation-Based Feature Selection (ACO-ML-CFS). This ACO performs a global search within the space of candidate features to identify an optimal subset. The selected feature subset is used by a Multi-Label k-Nearest Neighbors algorithm (ML-kNN), in this paper. The performance of ACO-ML-CFS is evaluated and compared with our previously works;Hill-Climbing algorithm for Multi-Label Correlation-Based Feature Selection (HC-ML-CFS) and Genetic algorithm for Multi-Label Correlation-Based Feature Selection (GA-ML-CFS) across four benchmark multi-label datasets. © 2025 IEEE.

关键词： Ant colony optimization

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Barber Optimization Algorithm:A New Human-Based Approach for Solving Optimization Problems

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Computers, Materials & Continua 2025年第5期83卷 2677-2718页

作者： Tareq Hamadneh Belal Batiha Omar Alsayyed Widi Aribowo Zeinab Montazeri Mohammad Dehghani Frank Werner Haider Ali Riyadh Kareem Jawad Ibraheem Kasim Ibraheem Kei Eguchi Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Department of Mathematics Faculty of Science and Information TechnologyJadara UniversityIrbid21110Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Electrical Engineering Faculty of Vocational StudiesUniversitas Negeri SurabayaSurabaya60231Indonesia Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Cybersecurity and Cloud computing Technical EngineeringUruk UniversityBaghdad10001Iraq Department of Medical Instrumentations Techniques Engineering Al-Rasheed University CollegeBaghdad10001Iraq Department of Electrical Engineering College of EngineeringUniversity of BaghdadBaghdad10001Iraq Department of Information Electronics Fukuoka Institute of TechnologyFukuoka8110295Japan

In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the *** processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for *** performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test *** comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark *** validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable *** results confirm BaOA’s versatility and reliability as an optimization *** study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.

关键词： Optimization metaheuristic barber hairstyle human-based algorithm exploration exploitation

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Performance Analysis of Text Classification Algorithms for Dhivehi Language Documents

Performance Analysis of Text Classification Algorithms for D...

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Advanced Research in computing (ICARC), International Conference on

作者： Fathimath Rashma Mohamed Prasanna S. Haddela Mohamed Jamaaludhin School Maldives Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

This study examines the effectiveness of various machine learning algorithms in classifying text written in “Dhivehi,” the official language of the Maldives. As a low-resource language with limited research in text analytics, “Dhivehi” poses unique challenges due to its distinctive linguistic properties. To address these challenges, this research evaluates the performance of algorithms, including Support Vector Machines, Naive Bayes, Decision Trees, Neural Networks, XGBoost, and Random Forest, leveraging a newly curated “Dhivehi” language dataset. The evaluation highlights that K-Neighbors achieved the highest performance, with an accuracy of 64.7% and F1 scores (macro: 0.640, weighted: 0.642), demonstrating a strong balance between precision and recall. Support Vector Machines (accuracy: 63.9%) and XGBoost (accuracy: 62.8%) also showed competitive results, with SVM slightly outperforming XGBoost in F1 metrics. Decision Tree exhibited the lowest performance across all metrics. The findings provide critical insights into improving text classification for low-resource languages and contribute to developing natural language processing tools adapted explicitly for “Dhivehi.” Furthermore, the dataset is publicly available on Mendeley data under the name “Dhivehi Categories data set” to foster future research and innovation in this domain.

关键词： Support vector machines Measurement Accuracy Computational modeling Text categorization Neural networks Nearest neighbor methods Bayes methods Decision trees Random forests

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Forecasting Prices of Strategic Food Products: Time Series Models in the Context of Post-Crisis Market Disruption

Forecasting Prices of Strategic Food Products: Time Series M...

引用

Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ahmed Harbaoui Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

In a post-crisis context, anticipating variations in raw material prices is essential for smart cities. At the beginning of this decade, two major global crises disrupted various market sectors: the COVID-19 pandemic and the Russian-Ukrainian war. Wheat is an essential product that contributes to food security in many countries. The price of this cereal fluctuated considerably during the two crises. Russia and Ukraine account for up to 28% of the wheat export market. Today, the wheat market seems to be gradually stabilizing from these disturbances. This paper explores reliable forecasting models to estimate wheat prices and support decision-making in the post-crisis period. To this end, two time-series forecasting techniques are compared: the Autoregressive Integrated Moving Average Model (ARIMA), known for its reliability in financial forecasting, and the Long Short-Term Memory Network (LSTM). The results reveal that the LSTM model achieves an accuracy of 97.49%, outperforming the ARIMA model, which registers an accuracy of 77%. These results underline the effectiveness of deep learning models for forecasting during periods of high price volatility, compared with conventional models.

关键词： Technological innovation Accuracy Smart cities Time series analysis Predictive models Registers Reliability Forecasting Long short term memory Context modeling

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