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IoT Network Traffic Classification Using machine learning algorithms: An Experimental Analysis

IEEE INTERNET OF THINGS JOURNAL 2022年第2期9卷 989-1008页

作者： Kumar, Rakesh Swarnkar, Mayank Singal, Gaurav Kumar, Neeraj Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 India Indian Inst Technol BHU Varanasi Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India Netaji Subhas Univ Technol Dept Comp Sci & Engn New Delhi 110078 India Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttarakhand India Asia Univ Dept Comp Sci & Informat Engn Taichung 413 Taiwan King Abdulaziz Univ Jeddah Saudi Arabia

Internet of Things (IoT) refers to a wide variety of embedded devices connected to the Internet, enabling them to transmit and share information in smart environments with each other. The regular monitoring of IoT network traffic generated from IoT devices is important for their proper functioning and detection of malicious activities. One such crucial activity is the classification of IoT devices in the network traffic. It enables the administrator to monitor the activities of IoT devices which can be useful for proper implementation of Quality of Service, detect malicious IoT devices, etc. In the literature, various methods are proposed for IoT traffic classification using various machine learning algorithms. However, the accuracy of these machine learning algorithms depends on the data generated from various IoT devices, features extracted from network traffic, site at which IoT is deployed, etc. Moreover, the selection of features and machine learning algorithms are manual operations that are prone to error. Therefore, it is important to study the network traffic characteristics as well as suitable machine learning algorithms for accurate and optimized IoT traffic classification. In this article, we perform an in-depth comparative analysis of various popular machine learning algorithms using different effective features extracted from IoT network traffic. We utilize a public data set having 20 days of network traces generated from 20 popular IoT devices. Network traces are first processed to extract the significant features. We then selected state-of-the-art machine learning algorithms based on the recent survey papers for the IoT traffic classification. We then comparatively evaluated the performance of those machine learning algorithms on the basis of classification accuracy, speed, training time, etc. Finally, we provided a few suggestions for selecting the machine learning algorithm for different use cases based on the obtained results.

关键词： Internet of Things (IoT) devices machine learning algorithms network flow network packet network traffic classification

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Comparative Analysis of machine learning algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy

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IEEE ACCESS 2024年 12卷 18939-18956页

作者： Yuliana, Hajiar Iskandar Hendrawan Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia

5G technology is a key factor in delivering faster and more reliable wireless connectivity. One crucial aspect in 5G network planning is coverage prediction, which enables network providers to optimize infrastructure deployment and deliver high-quality services to customers. This study conducts a comprehensive analysis of machine learning algorithms for 5G coverage prediction, focusing on dominant feature parameters and accuracy. Notably, the Random Forest algorithm demonstrates superior performance with an RMSE of 1.14 dB, MAE of 0.12, and R2 of 0.97. The CNN model, the standout among deep learning algorithms, achieves an RMSE of 0.289, MAE of 0.289, and R2 of 0.78, showcasing high accuracy in 5G coverage prediction. Random Forest models exhibit near-perfect metrics with 98.4% accuracy, precision, recall, and F1-score. Although CNN outperforms other deep learning models, it slightly trails Random Forest in performance. The research highlights that the final Random Forest and CNN models outperform other models and surpass those developed in previous studies. Notably, 2D Distance Tx Rx emerges as the most dominant feature parameter across all algorithms, significantly influencing 5G coverage prediction. The inclusion of horizontal and vertical distances further improves prediction results, surpassing previous studies. The study underscores the relevance of machine learning and deep learning algorithms in predicting 5G coverage and recommends their use in network development and optimization. In conclusion, while the Random Forest algorithm stands out as the optimal choice for 5G coverage prediction, deep learning algorithms, particularly CNN, offer viable alternatives, especially for spatial data derived from satellite images. These accurate predictions facilitate efficient resource allocation by network providers, ensuring high-quality services in the rapidly evolving landscape of 5G technology. A profound understanding of coverage prediction remains pivotal for suc

关键词： machine learning algorithms Prediction algorithms 5G mobile communication Deep learning machine learning Predictive models Classification algorithms Performance evaluation 5G coverage prediction classifier algorithm deep learning performance evaluation feature importance

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A Multiprocessing-Based Sensitivity Analysis of machine learning algorithms for Load Forecasting of Electric Power Distribution System

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IEEE ACCESS 2021年 9卷 31684-31694页

作者： Zainab, Ameema Syed, Dabeeruddin Ghrayeb, Ali Abu-Rub, Haitham Refaat, Shady S. Houchati, Mahdi Bouhali, Othmane Banales Lopez, Santiago Texas A&M Univ College Stn TX 77843 USA Texas A&M Univ Qatar Doha 77845 Qatar Qatar Sci & Technol Pk Iberdrola Innovat Middle East Doha 210000 Qatar

For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers' level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (similar to 24 million records) utilizing 32 processors.

关键词： Big data applications machine learning algorithms parallel processing load forecast smart grids

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Distinguishing lacto-fermented and fresh carrot slice images using the Multilayer Perceptron neural network and other machine learning algorithms from the groups of Functions, Meta, Trees, Lazy, Bayes and Rules

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2022年第9期248卷 2421-2429页

作者： Ropelewska, Ewa Natl Inst Hort Res Fruit & Vegetable Storage & Proc Dept Konstytucji 3 Maja 1-3 PL-96100 Skierniewice Poland

This study was aimed at evaluating the effect of spontaneous lacto-fermentation of carrot slices on flesh structure using different machine learning approaches. The textures computed from digital images of lacto-fermented and fresh carrot slices were compared using neural networks and other algorithms from different groups. In the case of Multilayer Perceptron, accuracies for training, testing and validation were considered. For some of the networks, lacto-fermented and fresh samples were completely distinguished. The accuracies for training, testing and validation were equal to 100%. For models built using other algorithms (LDA (Linear Discriminant Analysis), Multi Class Classifier, LMT (Logistic Model Tree), KStar, Naive Bayes, PART), the following metrics were used for the evaluation of model effectiveness: accuracies, time taken to build model, Kappa statistic, mean absolute error, root mean squared error, PRC (Precision-Recall) Area, ROC (Receiver Operating Characteristic) Area, MCC (Matthews Correlation Coefficient), F-Measure, Recall, Precision, FP (False Positive) Rate and TP (True Positive) Rate. The most satisfactory results were obtained for the LDA. The lacto-fermented and fresh carrot slices were distinguished with an average accuracy of 99%, low values of errors (mean absolute error: 0.0117, root mean squared error: 0.1014) and FP Rate (0.010). The weighted averages of other metrics were greater than or equal to 0.98 (Kappa statistic: 0.98, PRC Area: 0.987, ROC Area: 0.991, MCC: 0.980, F-Measure: 0.990, Recall: 0.990, Precision: 0.990, TP Rate: 0.990). The obtained results demonstrated the usefulness of different machine learning approaches to the evaluation of the effect of fermentation on changes in the carrot flesh structure.

关键词： Spontaneous lacto-fermentation Image textures Discrimination machine learning algorithms

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Evaluation of Tree-Based machine learning algorithms for Network Intrusion Detection in the Internet of Things

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IT PROFESSIONAL 2023年第5期25卷 45-56页

作者： Essa, Mohamed Saied Guirguis, Shawkat Kamal Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Informat Technol Alexandria 21526 Egypt Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Comp Sci & Informat Alexandria 21526 Egypt

The Internet of Things (IoT) is receiving increasing attention from academia and industry. However, improving the security of the IoT environment is critical for fostering trust in it and contributing to its growth in the manufacturing market. This study comparatively analyzes current methods for detecting intruders and malicious activities in IoT networks by introducing tree-based machine learning algorithms. It presents a research gap analysis of the current literature. Furthermore, an empirical evaluation study is presented to explore the potential of tree-based approaches to detect intruders in IoT networks. It compares the performance of bagging and boosting techniques in botnet detection by conducting an extensive experimental benchmarking.

关键词： Industries machine learning algorithms Botnet Network intrusion detection Benchmark testing Boosting Manufacturing

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Magnitude Type Conversion Models for Earthquakes in Turkey and Its Vicinity with machine learning algorithms

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JOURNAL OF EARTHQUAKE ENGINEERING 2023年第9期27卷 2533-2554页

作者： Coban, Kaan Hakan Sayil, Nilgun Karadeniz Tech Univ Engn Fac Dept Geophys Engn Trabzon Turkey

In this study, three new regression models are created for magnitude-type conversion with different machine learning algorithms (linear regression, regression trees, support vector machines, Gaussian process regression models, ensembles of trees) by using the earthquakes (M >= 4.0) that occurred in Turkey (1900-2020). Additionally, eight new equations are formed with linear and orthogonal regression methods. Developed equations and models are compared to equations selected from the literature by test data. As a result of the study, it is observed that machine learning algorithms create better models and provide results closer to the real values than created and selected equations.

关键词： machine learning algorithms regression trees support vector machines magnitude Regression

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Integrating water quality and streamflow into prediction of chemical dosage in a drinking water treatment plant using machine learning algorithms

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WATER SUPPLY 2022年第3期22卷 2803-2815页

作者： Wang, Hui Asefa, Tirusew Thornburgh, Jack Tampa Bay Water 2575 Enterprise Rd Clearwater FL 33763 USA Tampa Bay Water Syst Decis Support Clearwater FL 33763 USA

Understanding the relationship between raw water quality and chemical dosage is especially important for drinking water treatment plants (DWTP) that have multiple water sources where the ratio of different supply sources could change with seasons or in a matter of weeks in response to changing hydrologic conditions. In this study, the potential for deploying machine learning algorithms, including principal component regression (PCR), support vector regression (SVR) and long short-term memory (LSTM) neural network, are tested to build predictive models. These tools were used to estimate chemical dosage at daily time scale. Influent water quality such as pH, color, turbidity, and alkalinity, as well as chemical dosage including sulfuric acid, ferric sulfate and liquid oxygen were used to build and test these models. An 80/20 percent data split was used for training and testing model performance using correlation coefficients, relative mean square error, relative root mean square error and Nash-Sutcliffe efficiency. Results indicate, compared to PCR, both SVR and LSTM, were able to capture the nonlinear relationship between chemical dose and source water quality changes and displayed higher predictive skills. These types of models have application in real-time operational support without requiring computationally expensive physics-based models.

关键词： chemical dosage prediction domestic water supply drinking water treatment plants Long short-term memory neural network machine learning algorithms support vector machine

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GIS-based landslide susceptibility mapping of the Meghalaya-Shillong Plateau region using machine learning algorithms

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2023年第5期82卷 1-19页

作者： Agrawal, Navdeep Dixit, Jagabandhu Shiv Nadar Univ Disaster Management Lab Delhi NCR Gautam Buddha Nagar 201314 Uttar Pradesh India

Landslides are a common geological hazard causing impairment of public works and loss of lives worldwide and in India, especially in the Himalayan region. The present study aims to map the landslide susceptibility for the Shillong Plateau region of India using different machine learning algorithms, namely artificial neural network (ANN), extreme gradient boosting (XGBoost), K-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM) and provides insights into influential factors, with a focus on disaster risk reduction. For this purpose, the geospatial database containing 15 landslide conditioning factors related to regional geo-environmental settings and a landslide inventory with 1330 locations are prepared. The landslide susceptibility maps (LSM) reveal that the south-southeastern portion of Meghalaya, mainly slopes along the southern escarpment, are more susceptible to landslides. The model robustness is demonstrated using the area under the receiver operating characteristic curve (AUC), F1-score, kappa, and other statistical metrics. The XGBoost and RF machine learning models with AUC = 0.971 have shown the best performance, followed by SVM (0.958), KNN (0.951), and ANN (0.945), which is consistent with other applied statistical parameters and higher than the traditional MCDA methods. However, the problem of overestimation is observed in the case of ANN and XGBoost models. The generated LSMs will assist decision-makers and planners in identifying high-risk areas, prioritizing mitigation measures, and guiding regional development.

关键词： Landslide susceptibility assessment Landslide risk machine learning algorithms Geospatial analysis Northeast India

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Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2021年第9期59卷 1723-1734页

作者： Afify, Heba M. Zanaty, Muhammad S. Higher Inst Engn El Shorouk City Syst & Biomed Engn Dept Cairo Egypt Fac Comp & Informat Sci Cairo Egypt

The rapid spread of coronavirus disease (COVID-19) has become a worldwide pandemic and affected more than 15 million patients reported in 27 countries. Therefore, the computational biology carrying this virus that correlates with the human population urgently needs to be understood. In this paper, the classification of the human protein sequences of COVID-19, according to the country, is presented based on machine learning algorithms. The proposed model is based on distinguishing 9238 sequences using three stages, including data preprocessing, data labeling, and classification. In the first stage, data preprocessing's function converts the amino acids of COVID-19 protein sequences into eight groups of numbers based on the amino acids' volume and dipole. It is based on the conjoint triad (CT) method. In the second stage, there are two methods for labeling data from 27 countries from 0 to 26. The first method is based on selecting one number for each country according to the code numbers of countries, while the second method is based on binary elements for each country. According to their countries, machine learning algorithms are used to discover different COVID-19 protein sequences in the last stage. The obtained results demonstrate 100% accuracy, 100% sensitivity, and 90% specificity via the country-based binary labeling method with a linear support vector machine (SVM) classifier. Furthermore, with significant infection data, the USA is more prone to correct classification compared to other countries with fewer data. The unbalanced data for COVID-19 protein sequences is considered a major issue, especially as the US's available data represents 76% of a total of 9238 sequences. The proposed model will act as a prediction tool for the COVID-19 protein sequences in different countries.

关键词： COVID-19 protein sequences Conjoint triad (CT) machine learning algorithms Support vector machine (SVM)

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A Performance Comparison of machine learning algorithms for Load Forecasting in Smart Grid

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IEEE ACCESS 2022年 10卷 48419-48433页

作者： Alquthami, Thamer Zulfiqar, Muhammad Kamran, Muhammad Milyani, Ahmad H. Rasheed, Muhammad Babar King Abdulaziz Univ Elect & Comp Engn Dept Jeddah 21589 Saudi Arabia Univ Engn & Technol Lahore Dept Elect Engn Lahore 54890 Pakistan Univ Lahore Dept Elect & Elect Syst Lahore 54000 Pakistan Univ Alcala Escuela Politecn Super Madrid 28805 Spain

With the rapid increase in the world's population, the global electricity demand has increased drastically. Therefore, it is required to adopt efficient energy management mechanisms. Since the energy consumption trends are rather dynamic. Therefore, precise energy demand estimation and short and/or long-term forecasting results with higher accuracy are required to develop the optimization and control mechanism. Consequently, the machine learning (ML) techniques along with distributed demand response programs are being adopted to predict the future energy demand requirement with satisfactory results. In this paper, different state-of-the-art ML algorithms such as logistic regression (LR), support vector machines (SVM), naive Bayes (NB), decision tree classifier (DTC), K-nearest neighbor (KNN), and neural networks (NNs), have been implemented to analyze their performance. The main objective of this paper is to present a comparative analysis of ML algorithms for short-term load forecasting (STLF) regarding accuracy and forecast error. Based on the implementation and analysis, we have identified that, among other algorithms, the DTC provides comparatively better results. Therefore, we devised the enhanced DTC (EDTC) by integrating fitting function, loss function, and gradient boosting in DTC mathematical model for fine-tuning the control variables. The implementation results show that the proposed EDTC algorithm provides better forecast results (i.e., 99.9 % recall, 100% F1, 100% precision, 99.21 % training accuracy, and 99.70% testing accuracy.)

关键词： Forecasting Classification algorithms Prediction algorithms Support vector machines Stability analysis Predictive models Ground penetrating radar Smart grids electric load forecasting machine learning algorithms logistic regression decision tree

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