Modified Equilibrium Optimization Algorithm With Deep Learning-Based DDoS Attack Classification in 5G Networks

Abstract

5G networks offer high-speed, low-latency communication for various applications. As 5G networks introduce new capabilities and support a wide range of services, they also become more vulnerable to different kinds of cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. Effective DDoS attack classification in 5G networks is a critical aspect of ensuring the security, availability, and performance of these advanced communication infrastructures. In recent days, machine learning (ML) and deep learning (DL) models can be employed for an accurate DDoS attack detection process. In this aspect, this study designs a Modified Equilibrium Optimization Algorithm with Deep Learning based DDoS Attack Classification (MEOADL-ADC) method in 5G networks. The goal of the MEOADL-ADC technique is the automated classification of DDoS attacks in the 5G network. The MEOADL-ADC technique follows a three-stage process such as feature selection, classification, and hyperparameter tuning. Primarily, the MEOADL-ADC technique employs MEOA based feature selection approach. Next, the MEOADL-ADC technique utilizes the long short-term memory (LSTM) model for the classification of DDoS attacks. Finally, the tunicate swarm algorithm (TSA) is exploited to adjust the hyperparameter of the LSTM model. The design of MEOA-based feature selection and TSA-based hyperparameter tuning shows the novelty of the work. The experimental outcome of the MEOADL-ADC method is tested on a benchmark dataset, and the outcomes indicate the betterment of the MEOADL-ADC algorithm over the current methods with maximum accuracy of 97.60%.

Keywords

• 5G networks
• DDoS attack mitigation
• security
• deep learning
• feature selection
• tunicate swarm algorithm