A Novel Approach for Intrusion Detection System Using Deep Learning Architecture

Abstract

In recent years, network security has become increasingly complex due to rapid advancements in information and communication technologies. This complexity exposes systems to numerous potential threats. To tackle this challenge, we proposed an intrusion detection system achieving 99% accuracy and superior performance on critical metrics. This paper offers not only the authors' model but also a thorough comparative analysis. They evaluate established models and novel configurations combining diverse elements, algorithms, and deep learning models. Extensive training and testing on NSL KDD and UNSW NB15 datasets ensure comprehensive evaluation, providing a nuanced understanding of this system's performance. This research contributes significantly to network security and serves as a valuable reference for future studies in the field.