Year: 2022
Type: preprint
Abstract: Intruders can conduct increasingly devastating cyber-attacks because of the exponential rise of the Internet of Things (IoT) devices, which provides a wide attack surface for them. With malicious behavior, the intruder sought to deplete the target IoT network’s resources. We are using a machine learning algorithm in a well-designed dataset for IoT networks. Firstly we discussed some other intrusion detection datasets. Secondly, we are using the IoTID20 dataset and discussed its advantages from [1]. Thirdly we extract some important features from the dataset. Finally, we apply machine learning algorithms like linear Algorithm, Random forest, gradient boost Algorithm, etc.. to the dataset by which anomaly detection will be identified with high accuracy by using these learning techniques
Source: Research Square
Authors: Soumya Bajpai, Kapil Sharma
Institution: Amity University Madhya Pradesh
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4294044307