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Performance Evaluation of Container-Level Anomaly-Based Intrusion Detection Systems for Multi-Tenant Applications Using machine learning algorithms 21

Performance Evaluation of Container-Level Anomaly-Based Intr...

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16th International Conference on Availability, Reliability and Security (ARES)

作者： Cavalcanti, Marcos A. O. Inacio, Pedro R. M. Freire, Mario M. Inst Telecomunicacoes Covilha Portugal Univ Beira Interior Covilha Portugal

ISBN: (纸本)9781450390514

The virtualization of computing resources provided by containers has gained increasing attention and has been widely used in cloud computing. This new demand for container technology has been growing and the use of Docker and Kubernetes is considerable. According to recent technology surveys, containers are now mainstream. However, currently, one of the major challenges rises from the fact that multiple containers, with different owners, may cohabit on the same host. In container-based multi-tenant environments, security issues are of major concern. In this paper we investigate the performance of container-level anomaly-based intrusion detection systems for multi-tenant applications. We investigate the use of Bag of System Calls (BoSC) technique and the sliding window with the classifier and we consider eight machine learning algorithms for classification purposes. We show that among the eight machine learning algorithms, the best classification results are obtained with Decision Tree and Random Forest which lead to an F-Measure of 99.8%, using a sliding window with a size of 30 and the BoSC algorithm in both cases. We also show that, although both Decision Tree and Random Forest algorithms leads to the best classification results, the Decision Tree algorithm has a shorter execution time and consumes less CPU and memory than the Random Forest.

关键词： Containers Intrusion Detection Systems System Calls machine learning algorithms

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Assessing land cover change detection in a developing country with machine and deep learning algorithms: a case study

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2025年 1-21页

作者： Hmamou, Mohamed Chafiq, Tarik Ouhammou, Imrane Lmoudden, Othmane Ait Raji, Mohammed Ouaskit, Said Univ Hassan II Casablanca Fac Sci Ben MSik LTIM BP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik LCMP BP 7955 Casablanca Morocco Univ Hassan II Casablanca Fac Sci Ben MSik LGAGE BP 7955 Casablanca Morocco

This study examines the evolution of land cover in the Fez province of Morocco through a detailed analysis of Land Cover Change Detection (LCCD). Using satellite imagery from Landsat-8 and Landsat-9, we generated high-resolution land cover maps by applying a range of machine learning and deep learning techniques. The analysis spans the period from 2014 to 2022, aiming to identify the most suitable algorithm for accurate LCCD. The methods tested in this research include Support Vector machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Upon thorough comparison, SVM emerged as the top-performing model, yielding the most reliable classification results, while MLP demonstrated comparatively limited accuracy in this context. Our findings also underscore significant land use transitions, particularly an expansion of urban and industrial zones accompanied by a decline in agricultural lands trends largely attributed to anthropogenic pressures. These patterns are intricately linked to broader environmental shifts, including those related to climate. By analyzing spatial and temporal variations in land cover, this research contributes to a better grasp of the environmental transformations occurring in Fez, offering valuable knowledge to support evidence-based planning and the promotion of sustainable land use strategies.

关键词： Land cover classification GIS-based analysis Satellite image processing machine learning algorithms Deep learning models

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Classification of Swallowing Foods Using machine learning algorithms

Classification of Swallowing Foods Using Machine Learning Al...

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IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET)

作者： Lim, Ji Hyun Djuric, Petar M. Stanacevic, Milutin SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

ISBN: (纸本)9781665442312

There are limits in assessing healthy and abnormal swallowing by Videofluoroscopic swallowing study. Classification of accelerometric swallowing signals is much more efficient method to judge healthy swallowing. However, these methods have developed mostly with dual axis accelerometric signals and classifying two-class problems. This study is to examine classification methods with multi-class three-axis accelerometric signals. Swallowing signals of five foods are classified with both supervised learning algorithm and unsupervised learning algorithm. Three-axis signals denoised by 10-level discrete wavelet transform with soft thresholding before feature calculation. The result confirmed that classification with support vector machine and K-nearest neighbor can predict with 90% accuracy. However, Classification with fuzzy c-mean clustering produce low purity and normalized mutual information.

关键词： classification swallowing machine learning algorithms supervised learning unsupervised learning

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Evaluating machine learning algorithms for predicting house prices in Saudi Arabia

Evaluating machine learning algorithms for predicting house ...

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Smart Computing and Application (ICSCA), International Conference on

作者： Talal Alshammari Information and Computer Science Department College of Computer Science and Engineering University of Ha'il Ha'il Saudi Arabia

With real estate projects increasingly growing, it has a critical economic role in countries and a significant effect on economic and investment domains. There is limitation of researches have contributed the inclusive evaluation of machine learning algorithms in real estate price prediction in this field. machine learning algorithms could assist in forecasting real estate prices; thus, this paper presents a comparative assessment of machine learning algorithms to predict housing prices in Saudi Arabia. A real estate transaction datasets is used to evaluate the accuracy of machine learning algorithms. The datasets contains 13 columns and 59,846 instances (records). The results considerably vary in performance across the evaluated algorithms: random forest (RF), decision tree (DT) and linear regression (LR). After extensive examination the three proposed algorithm, Random Forest obtained R2-score 0.65, the decision tree obtained R2- score 0.30 and linear regression obtained R2-score 0.06. Random Forest performed better than the decision tree or linear regression methods.

关键词： Radio frequency Economics Measurement machine learning algorithms Linear regression Prediction algorithms Random forests

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Noise Reduction of ECG using Chebyshev filter and Classification using machine learning algorithms

Noise Reduction of ECG using Chebyshev filter and Classifica...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Prakash, M. Bhanu Sowmya, V Gopalakrishnan, E. A. Soman, K. P. Amrita Vishwa Vidyapeetham Amrita Sch Engn Ctr Computat Engn & Networking Coimbatore Tamil Nadu India

ISBN: (纸本)9781728185293

Cardiac disease detection is a tedious process. Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. The most important factor that limits the detection of cardiac disease is the rare availability of instances of the abnormal condition collected using ECG sensors. And if the signals contain noise, then the classification might become a challenging task. In this work, we address the problem of cardiac disease detection when the dataset has less number of noisy ECG sensor signals. Here, Chebyshev Type II filter and Chebyshev function, which is termed as Chebfun, are used. The Chebyshev filter is used for high-frequency noise removal and Chebfun is used to approximate the signal with its coefficients. These coefficients known as Chebfun coefficients are used as the features. These features are used for classification. In the proposed work, machine learning algorithms, like SVM, logistic regression, decision tree, and AdaBoost, are used for classifying the features extracted from Chebfun.

关键词： Ecg Noise Filtering machine learning algorithms Classification Chebfun Chebyshev Type II Filter

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Hannes Prosthesis Control Based on Regression machine learning algorithms

Hannes Prosthesis Control Based on Regression Machine Learni...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Di Domenico, D. Marinelli, A. Boccardo, N. Semprini, M. Lombardi, L. Canepa, M. Stedman, S. Bellingegni, A. Dellacasa Chiappalone, M. Gruppioni, E. Laffranchi, M. De Michieli, L. Ist Italiano Tecnol Rehab Technol Lab I-16163 Genoa Italy Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16145 Genoa Italy INAIL Prosthet Ctr I-40054 Bologna Italy

ISBN: (纸本)9781665417143

The quality of life for upper limb amputees can be greatly improved by the adoption of poly-articulated myoelectric prostheses. Typically, in these applications, a pattern recognition algorithm is used to control the system by converting the recorded electromyographic activity (EMG) into complex multi-degrees of freedom (DoFs) movements. However, there is currently a trade-off between the intuitiveness of the control and the number of active DoFs. We here address this challenge by performing simultaneous multi-joint control of the Hannes system and testing several state-of-the-art classifiers to decode hand and wrist movements. The algorithms discriminated multi-DoF movements from forearm EMG signals of 10 healthy subjects reproducing hand opening-closing, wrist flexion-extension and wrist pronation-supination. We first explored the effect of the number of employed EMG electrodes on device performance through the classifiers optimization in terms of F1Score. We further improved classifiers by tuning their respective hyperparameters in terms of the Embedding Optimization Factor. Finally, three mono-lateral amputees tested the optimized algorithms to intuitively and simultaneously control the Hannes system. We found that the algorithms performances were similar to that of healthy subjects, particularly identifying the Non-Linear Regression classifier as the ideal candidate for prosthetic applications.

关键词： Wrist Performance evaluation machine learning algorithms Control systems Electromyography Classification algorithms Optimization

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Prognosis of Stroke using machine learning algorithms

Prognosis of Stroke using Machine Learning Algorithms

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Kanaga Suba Raja. S B. Chandra K. Kausalya Ciddarth RM Gokul Ranjith V Easwari Engineering College Chennai Tamilnadu India Department of Information Technology Easwari Engineering College Chennai India

Stroke is an major causes of death and widest disability worldwide. It is one of great importance to predict the risk of having stroke for better prevention and early treatment. This brief report presents my attempt to develop a machine learning (ML) model to accurately and quickly predict whether or not a person suffered stroke based on the Random Forest dataset, K-Nearest Neighbor (KNN) Algorithm, Logistic *** the following process of classification and regression are proposed in the data moreover training of predefined hospital dataset are been performed. Exploratory Data Analysis (EDA) process has been implemented to investigating or discover the dataset outliers to achieve better classification. The main objective of this paper is to develop an effective stroke detection health care service to predict stroke in better performance. In addition to providing performance benchmark across various ML models, the report also examines which features are useful for stroke prediction.

关键词： Training machine learning algorithms Hospitals Predictive models Stroke (medical condition) Prediction algorithms Classification algorithms

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Estimating Forest Aboveground Biomass Using Gaofen-1 Images, Sentinel-1 Images, and machine learning algorithms: A Case Study of the Dabie Mountain Region, China

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REMOTE SENSING 2022年第1期14卷 176-176页

作者： Han, Haoshuang Wan, Rongrong Li, Bing Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Univ Chinese Acad Sci Coll Nanjing Nanjing 211135 Peoples R China

Quantitatively mapping forest aboveground biomass (AGB) is of great significance for the study of terrestrial carbon storage and global carbon cycles, and remote sensing-based data are a valuable source of estimating forest AGB. In this study, we evaluated the potential of machine learning algorithms (MLAs) by integrating Gaofen-1 (GF1) images, Sentinel-1 (S1) images, and topographic data for AGB estimation in the Dabie Mountain region, China. Variables extracted from GF1 and S1 images and digital elevation model data from sample plots were used to explain the field AGB value variations. The prediction capability of stepwise multiple regression and three MLAs, i.e., support vector machine (SVM), random forest (RF), and backpropagation neural network were compared. The results showed that the RF model achieved the highest prediction accuracy (R-2 = 0.70, RMSE = 16.26 t/ha), followed by the SVM model (R-2 = 0.66, RMSE = 18.03 t/ha) for the testing datasets. Some variables extracted from the GF1 images (e.g., normalized differential vegetation index, band 1-blue, the mean texture feature of band 3-red with windows of 3 x 3), S1 images (e.g., vertical transmit-horizontal receive and vertical transmit-vertical receive backscatter coefficient), and altitude had strong correlations with field AGB values (p < 0.01). Among the explanatory variables in MLAs, variables extracted from GF1 made a greater contribution to estimating forest AGB than those derived from S1 images. These results indicate the potential of the RF model for evaluating forest AGB by combining GF1 and S1, and that it could provide a reference for biomass estimation using multi-source images.

关键词： forest aboveground biomass multi-source remote sensing machine learning algorithms random forest Dabie Mountain region

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Comparative Performance Assessment Of machine learning algorithms To Predict Cardiovascular Disease

Comparative Performance Assessment Of Machine Learning Algor...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： S. Hemalatha T. Kavitha D. Niruba S. Nandhakumar R. Venkatesh Department of Computer Applications Kongu Engineering College Perundurai Erode

Cardio Vascular Disease (CVD) are a major factor in many deaths across the world. Many people in their middle or later years suffer from heart conditions, which typically result in significant health problems include heart attacks and *** order to stop any significant health problems before they arise, it is vital to detect and anticipate cardiac illnesses. In order to predict cardiovascular diseases at various evaluation stages, this paper used a variety of machine learning approaches, including Neural Networks (NN), Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector machines (SVM), K-Nearest Neighbor (KNN), and XG Boost. Results reveal that the Random Forest approach performed better than the other methods and had a predicted accuracy rating of 98.25%.

关键词： Support vector machines Radio frequency Heart machine learning algorithms Cardiac arrest Artificial neural networks Decision trees

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Constructive Analysis on Prediction and Detection of Diabetic Retinopathy (DR) using machine learning algorithms - A Generic Survey

Constructive Analysis on Prediction and Detection of Diabeti...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Chukka Keerthana Peram Tejasree Mudivarthi. Venkata Subba Rao R. S. Sai Pavan Kumar Prasanth Yalla Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India

Diabetic retinopathy is an eye disorder that affects people with diabetes. When high blood sugar levels harm the retina's blood vessels, the blood vessels may enlarge and leak or they may block the flow of blood. All these alterations may cause visual loss. In the current era, machine learning is very useful and plays a key role in medical applications. In this research, investigate the effectiveness and capability of different types of machine learning (ML) algorithms based diabetic retinopathy detection systems. The authors tested large quantities of retinal fundus images & gray scale pictures from Kaggle, Messidor, IEMRC and multiple available datasets. The application of an ensemble of machine learning classification algorithms to characteristics taken from the results of various retinal image processing algorithms is used to determine the presence of disease. The proposed goal is to anticipate the DR before it results in the worst scenario and prevent the patient from losing their visual acuity. The proposed method uses both supervised algorithms like Support Vector machine (SVM), ResNet and DensNet, Naive Bayes, K-Nearest Neighbors, and Neural Networks (NN), as well as unsupervised algorithms like k-means clustering, hierarchical clustering, and Markov chains to classify a variety of characteristics in DR. The best overall result is displayed by ResNet and DensNet with the accuracy of 96.22%.

关键词： Support vector machines Visualization machine learning algorithms Retinopathy Clustering algorithms Retina Prediction algorithms

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