Detection of DoS attacks using machine learning techniques
Deepak Kumar, Vinay Kukreja, Virender Kadyan, Mohit Mittal
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.