Hybrid CNN XGBoost intrusion detection approach tuned by modified sine cosine algorithm towards better cloud security

Abstract

Cloud computing (CC) delivers processing power and data storage on demand. It is one of the most significant computer science technologies, contributing to healthcare, industry, and the Internet of Things. One of CC's biggest security concerns are intrusion detection and separating harmful from legitimate communication, similar to computer networks. Although a wide range of intrusion detection systems is available today, they often suffer from misclassification issues, where the system can fail to recognize an attack as a threat or to mark normal traffic as malicious. This research proposes classifying network traffic using a convolutional neural network and extreme gradient boosting model. Additionally, a modified sine cosine algorithm is used to tune model hyperparameters for optimal performance. The presented framework was tested on major real-world TON IoT intrusion detection datasets. The proposed optimizer is compared to many recent metaheuristics in a matched experimental setting. The simulation results show that the suggested technique is superior to other methods for both datasets, with the best-performing optimized models achieving an accuracy of 96.667 on Windows 10 and 98.6731 on Windows 7 simulation.