Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

作     者：Bayi Xu Lei Sun Xiuqing Mao Chengwei Liu Zhiyi Ding 

作者机构：School of Cyber Science and EngineeringZhengzhou UniversityZhengzhou450000China Three AcademyInformation Engineering UniversityZhengzhou450001China The 3rd Research DepartmentNanjing Research Institute of Electronic EngineeringNanjing210007China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第2期

页      面：1995-2022页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Intrusion detection CNN BiLSTM BiGRU attention 

摘      要：In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace *** paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network *** have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention *** models have been evaluated for their detection performance on the NSL-KDD *** enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature ***,we address class imbalance in the dataset using focal ***,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection *** test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data *** binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.