A Hybridized- Logistic Regression and Deep Learning-based Approaches for Precise Anomaly Detection in Cloud

Abstract

Anomaly Detection plays a pivot role in determining the abnormal behaviour in the cloud domain. The objective of the manuscript is to present two approaches for Precise Anomaly Detection Approaches by hybridizing RBM with LR and SVM models. The various phases in the present approach are (a) Data collection (b) Pre-processing and normalization; OneHot Encoder for converting categorical values to numerical values followed by encoding the binary features through normalization (c) training the data (d) Building the Feedforward Deep Belief Network (EDBN) using hybridizing Restricted Boltzmann Machine (RBM) with Logistic Regression (LR) and Support Vector Machine (SVM); In the first approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using LR model. In the later approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using SVM model; both the approaches adopt unsupervised pre-training followed by supervised-fine-tuning operations (e) Model Evaluation using the significant parameters such as Precision, Recall, Accuracy, F1-score and Confusion Matrix. The experimental evaluations concluded the effective anomaly detection techniques by integrating the RBM with LR and SVM for capturing the intricate patterns and complex relationships among the data. The proposed approaches paves a path to improved anomaly detection technique, thereby enhancing the security features and anomaly monitoring systems across distinct domains.