An Intrusion Detection Model Based on Feature Selection and Improved One-Dimensional Convolutional Neural Network

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Qingfeng Li, Bo Li, Linzhi Wen
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引用次数: 0

Abstract

The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. This model first used the extreme gradient boosting decision tree (XGboost) algorithm to sort the preprocessed data, and then it used comparison to weed out 55 features with a higher contribution. Then, the extracted features were fed into the improved one-dimensional convolutional neural network (I1DCNN), and this network training was used to complete the final classification task. The feature selection and improved one-dimensional convolutional neural network (FS-I1DCNN) intrusion detection model not only solved the traditional machine learning method of relying on expert experience to extract features but also improved the detection efficiency of the model, reduced the training time while reducing the dimension, and increased the overall accuracy. In comparison to the I1DCNN model without feature extraction and the conventional one-dimensional convolutional neural network (1DCNN) model, the experimental results demonstrate that the FS-I1DCNN model’s overall accuracy increases by 0.67% and 2.94%, respectively. Its accuracy, precision, recall, and F1-score were significantly better than those of the other intrusion detection models, including SVM and DBN.
基于特征选择和改进型一维卷积神经网络的入侵检测模型
由于机器学习在网络安全领域的广泛应用,入侵检测问题有了新的解决方案,但目前仍存在一些问题。传统的机器学习入侵检测技术依赖专家经验来选择特征,深度学习方法的检测效率较低。本文提出了一种基于特征选择和改进一维卷积神经网络的入侵检测模型。该模型首先使用极梯度提升决策树(XGboost)算法对预处理后的数据进行排序,然后使用比较法剔除贡献度较高的 55 个特征。然后,将提取的特征输入改进的一维卷积神经网络(I1DCNN),并利用该网络训练完成最终的分类任务。特征选择和改进的一维卷积神经网络(FS-I1DCNN)入侵检测模型不仅解决了传统机器学习方法中依靠专家经验提取特征的问题,还提高了模型的检测效率,在减少维数的同时缩短了训练时间,提高了整体准确率。实验结果表明,与未进行特征提取的 I1DCNN 模型和传统的一维卷积神经网络(1DCNN)模型相比,FS-I1DCNN 模型的总体准确率分别提高了 0.67% 和 2.94%。其准确率、精确度、召回率和 F1 分数都明显优于其他入侵检测模型,包括 SVM 和 DBN。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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