A Many Objective-Based Feature Selection Model for Anomaly Detection in Cloud Environment

With the development of cloud computing technology (CCT), the processing of network traffic data becomes particularly important. However, the existing intrusion detection systems (IDS) are not efficient enough in analyzing network traffic data for anomaly detection. Therefore, this paper proposes a new data processing model for network anomaly detection. The model can simultaneously optimize the number of features (NF), accuracy, recall, false alarm rate (FAR) and precision. In order to better solve the model, an integrating dominance algorithm (MaOEA-ABC) with adaptive selection probability is proposed. In model, firstly, MaOEA-ABC is used to obtain the optimal feature subset by optimizing the above five objectives. Then, K-Nearest Neighbor (KNN) is used for network anomaly classification according to the optimal feature subset. Finally, MaOEA-ABC is compared with the existing standard MaOEAs algorithm (NSGA-III, EFR-RR, MaOEA-RD and PICEAg). The experimental results show that the approach can reduce the number of features on the basis of ensuring accuracy and FAR, thereby reducing the cost of detection.