Design and Development of Intrusive Detection Model based on Sequential Deep Recurrent Neural Network (SDRNN) Classifier

Abstract

Intrusion detection systems (IDS) must defend advanced communication networks. The systems' major goal was to identify broken patterns, signatures, and rules. IDS is crucial to network cyber defense. IDS can now detect both regular and atypical patterns thanks to advances in artificial intelligence, notably machine learning and deep learning. This research discusses a revolutionary deep learning and machine learning approach to intelligent categorization in IDS model development. The main objective is to detect network traffic irregularities utilizing HIKARI-2021. With an optimized feature set for the dataset, a deep learning model achieved 99.78% accuracy for binary and multi-class classification tasks.