A big data analytics for DDOS attack detection using optimized ensemble framework in Internet of Things

Abstract

Internet of Things (IoT) devices significantly threaten tech-dependant businesses and communities. With the rapid integration of IoT devices into local networks, a system to identify and prevent malicious activity on these devices is essential. These devices' inherent mobility, nature, and intelligence highlight the importance of establishing secure boundaries to safeguard against potential threats. Along with expanding the Internet's ability to handle traffic, the number of IoT devices linked to the Internet has also grown. Due to this modification, conventional methodologies and outdated data processing methods are no longer adequate for attack detection. Due to the growth in network traffic, it is more challenging to identify IoT attacks and malicious data in their early phases. This paper proposes a paradigm for identifying malicious network traffic. The framework uses two well-known classification-based techniques for detecting malicious network traffic, including a Support Vector Machine (SVM) and deep neural network model, i.e., a Convolutional Neural Network (CNN) embedded with a Gated Recurrent Unit (GRU), which is tuned using a Slime Mould Algorithm (SMA) for improved accuracy of 98.45% and 94.84%. The proposed technique is used to analyse the KDD dataset, and the results are assessed in terms of specificity, prediction time, training, and accuracy. The experimental findings suggest that our proposed model can detect DDoS attacks in less time than current solutions.