Filter-Based Ensemble Feature Selection and Deep Learning Model for Intrusion Detection in Cloud Computing

Abstract

In recent years, the high improvement in communication, Internet of Things (IoT) and cloud computing have begun complex questioning in security. Based on the development, cyberattacks can be increased since the present security techniques do not give optimal solutions. As a result, the authors of this paper created filter-based ensemble feature selection (FEFS) and employed a deep learning model (DLM) for cloud computing intrusion detection. Initially, the intrusion data were collected from the global datasets of KDDCup-99 and NSL-KDD. The data were utilized for validation of the proposed methodology. The collected database was utilized for feature selection to empower the intrusion prediction. The FEFS is a combination of three feature extraction processes: filter, wrapper and embedded algorithms. Based on the above feature extraction process, the essential features were selected for enabling the training process in the DLM. Finally, the classifier received the chosen features. The DLM is a combination of a recurrent neural network (RNN) and Tasmanian devil optimization (TDO). In the RNN, the optimal weighting parameter is selected with the assistance of the TDO. The proposed technique was implemented in MATLAB, and its effectiveness was assessed using performance metrics including sensitivity, F measure, precision, sensitivity, recall and accuracy. The proposed method was compared with the conventional techniques such as an RNN and deep neural network (DNN) and RNN–genetic algorithm (RNN-GA), respectively.

Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments

“…Kavitha et al [11] examined filter-based ensemble-FS (FEFS) and used the DL method to overcome the problems faced in CC. FEFS is an integration of three feature extraction approaches, namely, embedded, filter, and wrapper methods.…”

“…In Equation (11), θ represents the angle of attack in the range of [0, 2], R denotes the spiral radius, and u and v show the spiral constant and are fixed as 1. The equation to update the location of the sooty tern is as follows.…”

An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization

“…This can be achieved through various methods such as filter, wrapper, and embedded approaches. 49 Filter methods independently evaluate the importance of each feature based on a criterion, while wrapper methods use the classifier to assess the subset of features. Embedded methods are integrated with learning algorithms.…”

“…Feature selection involves identifying the most significant features to detect DDoS attacks. This can be achieved through various methods such as filter, wrapper, and embedded approaches 49 . Filter methods independently evaluate the importance of each feature based on a criterion, while wrapper methods use the classifier to assess the subset of features.…”

Intrusion detection system in cloud computing by utilizing VTR-HLSTM based on deep learning

“…The client computing paradigm has inherent issues with security, privacy, service availability, and other problems [5]. The cloud's resources and data connections are becoming increasingly challenging to manage, as the number of intrusions increase [6]. One of the techniques for preventing malicious attacks on cloud computing is intrusion detection.…”