Network intrusion detection: An optimized deep learning approach using big data analytics

Abstract

Managing enormous amounts of data, such as big data, and detecting network traffic intrusions are inefficiently handled by current computing technologies. Traditional analytical techniques cannot manage the incursions in continuous internet traffic and the enormous log data of server activity, leading to many inaccurate results and a prolonged training period. As a result, this research provides an efficient deep learning-based approach to enhance the attack identification task by addressing the basic big data complexity linked to many heterogeneous security data types. This framework employs a novel feature selection method incorporatingthe Aquila Optimizer (AO) and Fuzzy Entropy Mutual Information (FEMI) algorithms to pick distinctive characteristics. Subsequently, a modified canonical correlation-based technique is applied to combine selected characteristics. Then, the intrusion identification and categorization are carried out using the optimized ResNet152V2 method. Additionally, data augmentation using Auxiliary Classifier Generative Adversarial Network (ACGAN) is performed. Finally, we used the CICDDoS2019 and ToN-IoT datasets to validate the suggested methodology. By comparing the presented approach to several baseline methods, the effectiveness of the suggested methodology is assessed using various performance measures, including F1-score, recall, precision, accuracy, confusion matrix, and ROC curve. Finally, simulation results show that the suggested strategy is superior to other existing techniques and demonstrate that it is a resilient solution for network intrusion detection.