An Efficient Intrusion Detection System in Cloud Network Based on Deep Learning and Improved Marine Predators-Particle Swarm Optimization

Abstract

The growth of data is an ongoing phenomenon, driven by the expansion of cloud networks, the rapid development of computer systems, and the proliferation of highly beneficial applications. To defend networks from various attacks, such as denial-of-service (DoS), SlowHTTPTest, Hulk, Slowloris, and LOIC-type DDoS attacks, the area of intrusion detection has grown significantly. Traditional intrusion detection systems are combined with a variety of security methods to provide security. However, such a system has limitations when it comes to effectively analyzing vast amounts of data. To detect the vast amount of different intrusion attacks and to select the optimum features, an improved marine predators algorithm (IMPA) is combined with a swarm optimization algorithm named particle swarm optimization (PSO) is proposed. The experiments are conducted using the open-source available dataset referred to as the CSE-CIC-IDS2018 dataset. Here, the classification is done using the Bi-LSTM classifier, an improved version of the LSTM classifier. The experimental results showed that the performance of the proposed IMPAPSO-Bi-LSTM model has achieved high accuracy of 99.40%, recall of 98.20% and f1-score of 98.62% compared to the existing techniques such as recurrent convolutional neural network (R-CNN) and federated intrusion detection system integrated with the blockchain (FIDChain).