Title: Detection of DoS attacks using machine learning techniques

Authors: Deepak Kumar; Vinay Kukreja; Virender Kadyan; Mohit Mittal

Addresses: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Department of Informatics, School of Computer Science, University of Petroleum & Energy Studies (UPES), Bidholi, Dehradun 248007, India ' Department of Information Science, Kyoto Sangyo University, Kamigamo, Kyoto 603855, Japan

Abstract: 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.

Keywords: IoT threats; IoT; internet of things; IoT challenges; IDS; intrusion detection system; machine learning techniques.

DOI: 10.1504/IJVAS.2020.116448

International Journal of Vehicle Autonomous Systems, 2020 Vol.15 No.3/4, pp.256 - 270

Received: 26 Mar 2020
Accepted: 12 Jul 2020
Published online: 26 Jul 2021 *

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