网络入侵检测的回归分析

IF 2.2 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Data Pub Date : 2021-04-05 DOI:10.1145/3460620.3460751
Arun Nagaraja, U. Boregowda, V. Radhakrishna
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引用次数: 2

摘要

机器学习和统计学被归类为数据科学的一部分。回归是机器学习技术之一。在入侵检测方面,大多数文献的贡献主要是基于PCA、SVD、特征选择、特征约简技术和分类器算法的降维。现有文献对回归分析在入侵检测中的应用关注较少。基于回归的分析在入侵检测中有一定的应用范围。回归分析可用于降维、分类或预测任务。本文阐明了将回归分析应用于入侵检测的可能性,并概述了一些解决回归分析执行入侵检测的贡献。
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Regression analysis for network intrusion detection
Machine learning and statistics are categorized as part of data science. Regression is one of the techniques of machine learning. Most of the contributions in the literature in respect to intrusion detection are mainly based on dimensionality reduction using techniques such as PCA, SVD, feature selection, feature reduction techniques and application of classifier algorithms. Very less attention is paid on regression analysis for intrusion detection in the existing literature. There is a scope to apply regression-based analysis for intrusion detection. Regression analysis may be applied for dimensionality reduction, classification or prediction tasks. This paper throws light on the possibility of applying regression analysis for intrusion detection and outlines some of the contributions that addressed regression analysis to perform intrusion detection.
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来源期刊
Data
Data Decision Sciences-Information Systems and Management
CiteScore
4.30
自引率
3.80%
发文量
0
审稿时长
10 weeks
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