A New IDS for Detecting DDoS Attacks in Wireless Networks using Spotted Hyena Optimization and Fuzzy Temporal CNN

Abstract

Cyber-attacks are rapidly increasing in the internet era due to the growth of information technology. The distributed denial of service (DDoS) attacks are increasing dramatically due to the distributed services in cloud networks. In this paper, a new Intrusion Detection System (IDS) is proposed to improve the performance of the networks by detecting DDoS attacks effectively in wireless networks. In this work, we propose a new feature selection method called Split Filter Feature Selection and Spotted Hyena Optimization Based Feature Optimization Method (SFSH-FOM) to select the most contributed features that are helpful for enhancing the classification accuracy. In this work, a new deep learning algorithm named Fuzzy Temporal Features incorporated Convolutional Neural Network (FT-CNN) is proposed for performing effective classification. Here, a new cross layer feature fusion technique is also proposed by using FT-CNN and LSTM for enhancing the performance. The experiments have been carried out to evaluate the proposed IDS using the standard datasets, namely the KDD'99 dataset, the NSL-KDD dataset, and the DDoS dataset by considering the evaluation metrics such as detection accuracy, recall, precision, and F1-score, and it has also been proved as better than other IDSs in terms of accuracy and false alarm rate.