Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.
Water drilling machines are used to drill boreholes in the ground to extract groundwater. The resources required for water drilling vary from region to region due to underground water table depth and ground soil layer. Water drilling on a hard underground soil layer requires different resources than a soft underground. The proposed study facilitates the drilling industry by selecting the region with a soft land layer and increasing the penetration rate. Furthermore, the number of days and water table depth prediction allows the drilling industry to estimate the depth of the water table and time resources to reach the water table at different locations. The classification techniques classify the region based on the soil land layer. Regression techniques are used for predicting water table depth and number of days. The experiments are performed on a borehole log dataset provided by a research organization. This study used Support Vector Machine, TabNet, and Deep Tabular models to predict the land soil layer and compare the results with our proposed Ensemble Weighted Voting Soil Layer Classifier (EWV-SLC). The performance of the classification model is evaluated using accuracy, Precision, Recall, and F1 Score. The experimental finding shows that the EWV-SLC model performs better in accuracy and F1 score than other machine learning techniques. The performance of the regression model is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE),Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). In a days and water table depth prediction phase Support, Vector Regressor, Deep Neural Network, and TabNet Regressor are used, and compare the results with our proposed Ensemble Number of Days (E-NOD) and Ensemble Water Table depth (E-WTD) Regressor model. E-NOD and E-WTD models achieved less MAE, RMSE, and MSE than other machine learning methods. INDEX TERMS Applied machine learning, ensemble learning, voting classifier, deep tabular model. The associate editor coordinating the review of this manuscript and approving it for publication was Ikramullah Lali.
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