基于物联网大数据的入侵检测系统综述

Imane Laassar, Moulay Youssef Hadi
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引用次数: 2

摘要

网络安全是现代社会最令人担忧的问题之一。近年来,人们对入侵检测系统进行了大量的研究。然而,网络安全是现代时代最重要的担忧之一,这是由于过去一段时间内快速发展和大量使用改变的技术。这些技术的安全漏洞已经成为一个主要的争议点,入侵检测系统用于对安全网络上未经批准的访问和异常攻击进行分类。入侵检测系统的实现采用了不同的方法,机器学习技术就是其中之一。为了了解机器学习技术在解决基于物联网(IoT)的大数据入侵发现异常中的应用现状,本文进行了综述。从2010年到2021年共总结了55篇围绕单一、混合和协作分类器设计方式的论文。这篇综述文章还包括一些基本信息,如物联网,大数据和机器学习方法的讨论。
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Intrusion detection systems for internet of thing based big data: a review
Network security is one of the foremost anxieties of the modern time. Over the previous years, numerous studies have been accompanied on the intrusion detection system. However, network security is one of the foremost apprehensions of the modern era this is due to the speedy development and substantial usage of altered technologies over the past period. The vulnerabilities of these technologies security have become a main dispute intrusion detection system is used to classify unapproved access and unusual attacks over the secured networks. For the implementation of intrusion detection system different approaches are used machine learning technique is one of them. In order to comprehend the present station of application of machine learning techniques for solving the intrusion discovery anomalies in internet of thing (IoT) based big data this review paper conducted. Total 55 papers are summarized from 2010 and 2021 which were centering on the manner of the single, hybrid and collaborative classifier design. This review paper also includes some of the basic information like IoT, big data, and machine learning approaches are discussed.
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