Detection of DoS attacks using machine learning techniques

被引：6

作者：

Kumar D. ^{[1
]}

Kukreja V. ^{[1
]}

Kadyan V. ^{[2
]}

Mittal M. ^{[3
]}

机构：

[1] Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab

[2] Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies (UPES), Bidholi Dehradun

[3] Department of Information Science, Kyoto Sangyo University, Kamigamo, Kyoto

来源：

International Journal of Vehicle Autonomous Systems | 2020年 / 15卷 / 3-4期

关键词：

IDS; Internet of things; Intrusion detection system; IoT; IoT challenges; IoT threats; Machine learning techniques;

D O I：

10.1504/IJVAS.2020.116448

中图分类号：

学科分类号：

摘要：

As the growth of IoT has been further reinforced by the advances, when used with other technologies like embedded systems, hardware and software enhancements, networking devices, but still there are so many threats in IoT that includes security, accuracy, performance, networks, and privacy. With the increased use of smart services, remote access, and frequent changes in networks has raised many security and privacy concerns. Therefore, security threats in IoT are one of the main issues while data transmission. Thus, network challenges and security issues concerning to IoT can be resolved by using machine learning (ML) techniques and algorithms. The current study outlined the security standards for IoT applications to enhance the performance and efficiency of the network and user services. As well as, the study focus is on comparing the Support Vector Machine (SVM) and Decision Trees for the detection of Denial of Service (DoS) attacks. Copyright © 2020 Inderscience Enterprises Ltd.

引用

页码：256 / 270

页数：14

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