Design and Implementation of Network Intrusion Detection System ( NIDS ) Based on Deep Learning
Wenzhi Wang, Zhanqiao Liu · Security and Privacy · 2025
ABSTRACT Network intrusion detection systems (NIDS), a crucial part of overall network security, are experiencing previously unheard‐of difficulties as a result of the quick advancement of technological developments. The complex consists and evolving threats are too much for conventional signature‐based and anomaly‐based intrusion detection techniques to handle. This paper suggests DeepNIDS, a deep learning‐based network intrusion detection system, to enhance NIDS performance. The method reduces the false alarm rate and enhances the ability to recognize unexpected assaults by combining algorithms such as generative adversarial networks (GANs), long short‐term memory networks (LSTMs), and convolutional neural networks (CNNs). Using public datasets including KDD Cup 99, CICIDS 2017, and NSL‐KDD, experimental evaluation proves that DeepNIDS not only outperforms other deep learning baseline models and traditional models with a detection rate of 96.8%, but also does well in terms of false alarm rate. Furthermore, DeepNIDS exhibits strong computational efficiency and resilience, making it appropriate for processing massive network traffic in real time. According to the report, deep learning technology offers fresh concepts for creating effective NIDS and is anticipated to play a significant role in network security protection in the future.