An Intrusion Detection System in IoT Environment Using KNN and SVM Classifiers

Q2 Social Sciences
Abdulmalik M Alfarshouti, Saad M. Almutairi
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引用次数: 2

Abstract

IoT applications are now used in most applications in this world to facilitate data collection and remote and automatic management of all modern devices. Due to the large spread of these devices in multiple regions, they become easily vulnerable to penetration by many types of attacks. This research will focus on network layer denial of service (DOS) attacks to detect. This type of attack was chosen because of its danger to the availability of services, such as e-commerce services, financial and government services, as well as educational organizations. Failure to provide these services frequently leads to huge financial losses in addition to loss of confidence in these organizations. Machine learning techniques will be used in the proposed research to detect these attacks in a fast and efficient manner.
基于KNN和SVM分类器的物联网环境下入侵检测系统
物联网应用程序现在用于世界上大多数应用程序,以促进所有现代设备的数据收集和远程自动管理。由于这些设备在多个地区广泛分布,因此很容易受到多种攻击的渗透。本研究将重点对网络层拒绝服务(DOS)攻击进行检测。选择这种类型的攻击是因为它对服务的可用性有危险,例如电子商务服务、金融和政府服务以及教育机构。如果不能提供这些服务,除了丧失对这些组织的信心外,还经常造成巨大的财务损失。机器学习技术将在拟议的研究中使用,以快速有效的方式检测这些攻击。
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
Webology
Webology Social Sciences-Library and Information Sciences
自引率
0.00%
发文量
374
审稿时长
10 weeks
期刊介绍: Webology is an international peer-reviewed journal in English devoted to the field of the World Wide Web and serves as a forum for discussion and experimentation. It serves as a forum for new research in information dissemination and communication processes in general, and in the context of the World Wide Web in particular. Concerns include the production, gathering, recording, processing, storing, representing, sharing, transmitting, retrieving, distribution, and dissemination of information, as well as its social and cultural impacts. There is a strong emphasis on the Web and new information technologies. Special topic issues are also often seen.
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