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A comprehensive intrusion detection framework using boosting algorithms

COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷 107869-107869页

作者： Kilincer, Ilhan Firat Ertam, Fatih Sengur, Abdulkadir Firat Univ Technol Fac Dept Elect Elect Engn Elazig Turkey Firat Univ Technol Fac Dept Digital Forens Engn Elazig Turkey

Intrusion Detection Systems are one of the most effective technologies that protect systems against cyber-attacks. In this study, a new Comprehensive Cyber Security Intrusion Detection Dataset (CCiDD) was created. The CCiDD_A and CCiDD_B datasets are derived from the created dataset. Two datasets were compared with the NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets. In the study, the most optimal features for all datasets were determined by the Extra Tree algorithm and the new sub-datasets were classified by machine learning methods with default parameters. As a result of the classification, LGBM and XGBoost algorithms were selected as the most successful algorithms. Hyper parameter optimization was applied to LGBM and XGBoost algorithms to increase classification performance. LGBM classifier surpassed XGBoost classifier in terms of performance and processing time. LGBM algorithm achieved performance values of 99.84%, 98.02%, 99.94%, 95.68% and 99.98% for NSL-KDD, UNSW-NB15, CSE-CICIDS2018, CCiDD_A and CCiDD_B datasets, respectively. Since detection time of attacks is a critical issue, the LGBM classifier is recommended for attack detection in terms of time and performance.

关键词： IDS boosting algorithms Extra tree algorithm Cyber security Machine learning

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An overview of cardiovascular disease infection: A comparative analysis of boosting algorithms and some single based classifiers

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Statistical Journal of the IAOS 2020年第4期36卷 1189-1198页

作者： Adeboye, Nureni Olawale Abimbola, Olawale Victor Department of Mathematics and Statistics Federal Polythechnic Ilaro Nigeria

Machine learning is a branch of artificial intelligence that helps machines learn from observational data without being explicitly programmed and its methods have been found to be very useful in the modern age for medical diagnosis and for early detection of diseases. According to the World Health Organization, 12 million deaths occur annually due to heart-related diseases. Thus, its early detection and treatment are of interest. This research introduces a better way of improving the timely prediction of cardiovascular diseases in suspected patients by comparing the efficiency of two boosting algorithms with four (4) other single based classifiers on cardiovascular official data. The best model was selected based on performances of 5 different evaluation metrics. From the results, Adaptive boosting is seen to outperform all other algorithms with a classification accuracy of 74.2%, closely followed by gradient boosting. However, gradient boosting was chosen as an acceptable technique because it trains faster than Adaboost with a better precision of 74.9% compared to 74.7% exhibited by Adaboost. Thus boosting algorithms are better predictors compared to single based classifiers with factors of age, systolic blood pressure, weight, cholesterol, height, and diastolic blood pressure as the major contributors to the model building. © 2020 - IOS Press and the authors. All rights reserved.

关键词： AdaBoost boosting algorithms Cardiovascular diseases ensemble gradient boosting

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CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling

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BIOINFORMATICS 2018年第18期34卷 3069-3077页

作者： Peng, Hui Zheng, Yi Blumenstein, Michael Tao, Dacheng Li, Jinyan Univ Technol Sydney Fac Engn & Informat Technol Adv Analyt Inst Broadway NSW 2007 Australia Univ Sydney Fac Engn & Informat Technol Sch Informat Technol Darlington NSW 2008 Australia

Motivation: CRISPR/Cas9 system is a widely used genome editing tool. A prediction problem of great interests for this system is: how to select optimal single-guide RNAs (sgRNAs), such that its cleavage efficiency is high meanwhile the off-target effect is low. Results: This work proposed a two-step averaging method (TSAM) for the regression of cleavage efficiencies of a set of sgRNAs by averaging the predicted efficiency scores of a boosting algorithm and those by a support vector machine (SVM). We also proposed to use profiled Markov properties as novel features to capture the global characteristics of sgRNAs. These new features are combined with the outstanding features ranked by the boosting algorithm for the training of the SVM regressor. TSAM improved the mean Spearman correlation coefficiencies comparing with the state-of-the-art performance on benchmark datasets containing thousands of human, mouse and zebrafish sgRNAs. Our method can be also converted to make binary distinctions between efficient and inefficient sgRNAs with superior performance to the existing methods. The analysis reveals that highly efficient sgRNAs have lower melting temperature at the middle of the spacer, cut at 5'-end closer parts of the genome and contain more 'A' but less 'G' comparing with inefficient ones. Comprehensive further analysis also demonstrates that our tool can predict an sgRNA's cutting efficiency with consistently good performance no matter it is expressed from an U6 promoter in cells or from a T7 promoter in vitro.

关键词： CRISPRS (Genetics) boosting algorithms MARKOV processes SUPPORT vector machines RANK correlation (Statistics)

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Diabetes classification model based on boosting algorithms

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BMC BIOINFORMATICS 2018年第20180327期19卷 109-109页

作者： Chen, Peihua Pan, Chuandi Wenzhou Med Univ Inst Biopharmaceut Informat & Technol Wenzhou Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Comp Technol & Informat Management Wenzhou City Peoples R China

Background: Diabetes mellitus is a common and complicated chronic lifelong disease. Hence, it is of high clinical significance to find the most relevant clinical indexes and to perform efficient computer-aided pre-diagnoses and diagnoses. Results: Non-parametric statistical testing is performed on hundreds of medical measurement index results between diabetic and non-diabetic populations. Two common boosting algorithms, Adaboost.M1 and LogitBoost, are selected to establish a machine model for diabetes diagnosis based on these clinical test data, involving a total of 35,669 individuals. The machine classification models built by these two algorithms have very good classification ability. Here, the LogitBoost classification model is slightly better than the Adaboost. M1 classification model. The overall accuracy of the LogitBoost classification model reached 95.30% when using 10-fold cross validation. The true positive, true negative, false positive, and false negative rates of the binary classification model were 0.921, 0.969, 0.031, and 0.079, respectively, and the area under the receiver operating characteristic curve reached 0.99. Conclusions: The boosting algorithms show excellent performance for the diabetes classification models based on clinical medical data. The coefficient matrix of the original data is a sparse matrix, because some of the test results were missing, including some that were directly related to disease diagnosis. Therefore, the model is robust and has a degree of pre-diagnosis function. In the process of selecting the preferred test items, the most statistically significant discriminating factors between the diabetic and general populations were obtained and can be used as reference risk factors for diabetes mellitus.

关键词： Diabetes boosting algorithms Computer-aided diagnoses

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Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 3221-3232页

作者： Deliang Sun Jing Wang Haijia Wen YueKai Ding Changlin Mi Key Laboratory of GIS Application Research Chongqing Normal UniversityChongqing 401331China Key Laboratory of New Technology for Construction of Cities in Mountain Area Ministry of EducationChongqing UniversityChongqing 400045China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing UniversityChongqing 400045China School of Civil Engineering Chongqing UniversityChongqing 400045China Natural Resources Development Service Center of Linyi Linyi 276000China

boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)***,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM *** investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average *** XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM ***,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM *** boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer ***,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer *** HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test *** model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study *** study offers a scientific reference for LSM and disaster prevention *** study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou *** proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM ***,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.

关键词： Landslide susceptibility Hyperparameter optimization boosting algorithms SHapley additive exPlanations(SHAP)

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Vibration analysis of embedded porous nanobeams under thermal effects using boosting machine learning algorithms and semi-analytical approach

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年第29期31卷 12320-12343页

作者： Tariq, Aiman Uzun, Busra Deliktas, Babur Yayli, Mustafa Ozgur Bursa Uludag Univ Engn Fac Dept Civil Engn Bursa Turkiye

This study presents a thermal vibration analysis of functionally graded porous nanobeams using boosting machine learning models and a semi-analytical approach. Nonlocal strain gradient theory is employed to explore vibration behavior, accounting for thermal and size effects. A semi-analytical approach solution utilizing Fourier series and Stokes' transform to establish an eigenvalue problem capable of examining vibrational frequencies of porous nanobeams in both rigid and deformable boundary conditions is presented. Four boosting models including gradient boosting (GBoost), light gradient boosting (LGBoost), extreme gradient boosting (LGBoost), and adaptive boosting (AdaBoost) are employed to study the impact of seven crucial parameters on natural frequencies of a nanobeam. Sobol quasi-random space-filling method is used to generate the samples by varying input feature combinations for different porous nanobeam distributions. The model performance is assessed using statistical metrics, visualization tools, 5-fold cross-validation, and SHAP analysis for feature importance. The results highlight the effectiveness of boosting ML models in predicting natural frequencies, particularly XGBoost, which achieved an exceptional R2 value of 0.999, accompanied by the lowest MAE, MAPE, and RMSE values among the models assessed. LGBoost and AdaBoost follow XGBoost in performance, while GBoost exhibits relatively lower effectiveness, as highlighted by radar plots. The SHAP analysis revealed the significant impact of the foundation parameter on frequency prediction, with porosity coefficients notably influencing higher vibration modes.

关键词： Porous nanobeams vibration analysis boosting algorithms size effect SHAP analysis

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boosting machine learning algorithms for predicting the macroscopic material behavior of continuous fiber reinforced composite

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JOURNAL OF REINFORCED PLASTICS AND COMPOSITES 2024年

作者： Tariq, Aiman Polat, Ayse Deliktas, Babur Bursa Uludag Univ Engn Fac Dept Civil Engn Gorukle CampusNilufer TR-16059 Bursa Turkiye

Macroscopic mechanical properties of fibrous materials are often characterized by modeling their microscale behavior using micromechanical techniques. This process typically involves using a Representative Volume Element (RVE) and finite element simulations to obtain the macroscopic behavior through homogenization. However, these micromechanical simulations can be computationally demanding, especially for 3D models with discrete material microstructures. This paper uses boosting machine learning algorithms to predict the homogenized macroscopic material behavior of heterogeneous composites. These models are trained on the micromechanical simulation results generated by varying the constitutive parameters of local phases and microscopic parameters such as fiber volume fraction. The Bayesian optimization is used to determine the best hyperparameters of the considered boosting models, which include adaptive boosting (AdaB), gradient boosting (GBR), light gradient boosting (LGB), and extreme gradient boosting (XGB). The performances of trained models are assessed using various metrics such as R2, MAE, MAPE, and RMSE and using various plots such as scatter plots, Taylor plots, radar plots, and bar plots. The comparative assessment showed that all the models predicted the homogenized stiffness matrix of the RVE successfully, with R2 values between 0.94 and 0.99. The XGB model presented the best overall performance. This work contributes to the field of composites by presenting a new and computationally efficient approach to predict the macroscopic behavior of RVEs using boosting models.

关键词： boosting algorithms fiber reinforced composite homogenization representative volume elements

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Prediction of nonlinear dynamic responses and generation of seismic fragility curves for steel moment frames using boosting machine learning techniques

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COMPUTERS & STRUCTURES 2024年 305卷

作者： Zareian, Farzaneh Banazadeh, Mehdi Zareian, Mohammad Sajjad Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran Shahab Danesh Univ Dept Civil Engn Qom Iran

The main objective of this paper is to develop machine learning (ML) models for predicting the seismic responses of steel moment frames. For this purpose, four boosting ML techniques-gradient boosting, XGBoost, LightGBM, and CatBoost-were developed in Python. To create an inclusive dataset, 92,400 nonlinear time-history analyses were performed on 1,848 steel moment frames under 50 earthquakes using OpenSeesPy. Geometric configurations, structural properties, and ground motion intensity measures were considered as the inputs for the models. The outputs included maximum global drift ratio (MGDR), maximum interstory drift ratio (MIDR), base shear coefficient (BSC), and maximum floor acceleration (MFA). The study also investigated the effectiveness of the ML models in estimating fragility curves for an 8-story steel frame at different performance levels. Finally, a web application was developed to facilitate the estimation of the peak dynamic responses for steel moment frames. The results show that the LightGBM and CatBoost models demonstrate superior predictive performance, with coefficient of determinations (R2) higher than 0.925. Furthermore, the LightGBM models can estimate the fragility curves with minimal errors (e.g., the relative errors in the median values of the predicted curves are less than 10%).

关键词： Machine learning Nonlinear dynamic responses Fragility curves Steel moment frames boosting algorithms

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boosting the visibility of services in microservice architecture

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3099-3111页

作者： Tokmak, Ahmet Vedat Akbulut, Akhan Catal, Cagatay Istanbul Kultur Univ Dept Comp Engn TR-34536 Istanbul Turkiye Qatar Univ Dept Comp Sci & Engn Doha 2713 Qatar

Monolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.

关键词： Service discovery Microservice architecture boosting algorithms CatBoost LightGBM XGBoost Gradient increase

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Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs

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MARINE AND PETROLEUM GEOLOGY 2022年 145卷

作者： Al-Mudhafar, Watheq J. Abbas, Mohammed A. Wood, David A. Basrah Oil Co Basrah Iraq Basrah Oil Co Petrophys Basrah Iraq DWA Energy Ltd Lincoln England

Lithofacies classification from well logs recorded through heterogeneous carbonate reservoirs helps to improve reservoir discrimination with respect to fluid flow and storage capabilities. A novel technique is developed to classify carbonate lithofacies with the assistance of boosting algorithms for a well drilled into the large Majnoon oil and gas field of southern Iraq. Five boosting machine learning algorithms: Logistic boosting Regression (LogitBoost), Generalized boosting Modeling (GBM), Extreme Gradient boosting (XGBoost), Adaptive boosting Model (AdaBoost), and K-nearest neighbor (KNN) were comparatively implemented to attain the most realistic lithofacies predictions. Data from available cores provide actual lithofacies determination. Additionally, seven standard well logs recorded provide continuous additional information across the complete reservoir section. Random sub-sampling of the well-log dataset was used to train and test the models. Confusion matrices of the results provided Total Percent Correct (TPC) accuracy measures for LogitBoost, GBM, XGBoost, AdaBoost, and KNN with respect to the entire dataset of 97.65%, 96.99%, 97.74%, 97.74%, and 96.74%, respectively, and with respect to the validation subsets TPC was 94%, 96%, 98%, 91%, and 96%, respectively. The XGBoost provided more accurate lithofacies classification than the other five boosting algorithms with respect to the predictions generated for both the entire dataset and testing subset. The lithofacies predictions were then compared with poro-perm interpretations from the Nuclear Magnetic Resonance (NMR) log and oil rate from the Production Logging Tool (PLT). Rudist-rich and argillaceous lithofacies display lower porosity and permeability, respec-tively. The Rudist-rich lithotype is associated with high-production-rate zones, whereas the argillaceous facies is associated with low-production-rate zones. The boosting machine learning workflow developed efficiently provides accurate lithofacies

关键词： Reservoir characterization Lithofacies classification boosting algorithms Machine learning Carbonate reservoirs Well log data analysis

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