An efficient hybrid deep learning approach for internet security - Ertam, Fatih (Elsevier, 2019)
Nowadays, Internet is mostly used communication tool worldwide. However, the major problem of the Internet is to provide security. To provide internet security, many researches and papers have been suggested about information and network security. The commonly used system against network attacks is firewalls. In this study, a novel firewall data classification approach is presented. This approach uses 10 cases to obtain numerical results. The proposed approach consists of data acquisition from Firewall, feature selection and classification steps. Firstly, the Firewall data were gathered from a Firewall. Then, the redundant features are eliminated and these features are normalized using min-max normalization. The obtained final feature sets are forwarded to classifiers. In the cases defined, Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (Bi-LSTM) and Support Vector Machine (SVM) are utilized as classifiers. It was seen from the results, the deep learning approach are more successful than SVM classifier and the highest classification accuracy was calculated as 97.38% by using Bi-LSTM-LSTM hybrid network. The proposed method has several advantages and these are (1) the proposed method achieved high success rates using hybrid deep learning approaches (2) the training time of the proposed method is short (3) an intelligent network security monitoring method is presented using basic methods and deep learning. In addition, a useful approach has been presented to achieve high success rate at the end of the faster training process than traditional machine learning methods. Briefly, an intelligent monitoring system is proposed for network security. (C) 2019 Elsevier B.V. All rights reserved.
Keywords: Deep learning; LSTM; Bi-LSTM; Network security; Classification; Big data