Document Type : Reseach Article
Authors
1 Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.
2 Department of Computer engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Abstract
The concept of Internet of Things (IoT) and its countless applications are considered as an inseparable part of modern technology era. Security plays a critical role in IoT applications due to pervasiveness of the IoT in all of the aspects in daily life. from the other hand final devices such as their limited computing power, communication constraints, large number of devices connected to each other, and communication between devices and users do not allow for using traditional methods to solve security issues. Intrusion detection systems (IDSs) which can separate malicious traffic from normal mode are among the effective solutions in this field. the installed IDS should be highly accurate and light weight to affect accuracy. In order to bring services closer to electronic devices, a concept called "fog" has emerged. A large number of studies have been conducted to make the light IDS for IoT network utilizing various methods. The present study aims to propose two-layer hierarchical IDS based on machine learning, which detects attacks by considering the limitations of IoT resources. In order to create an efficient and accurate IDS, the combination of two improved K-Nearest Neighbor algorithms (KNN) applied in the fog to separate malicious traffic from normal mode and Multi-Layer Perceptron neural network (MLP) applied in the cloud to determine type of the attacks, respectively. we evaluated our proposed method using IOT23 dataset. The results prove the improvement in accuracy about 99.9%, and in time complexity compared to the previous methods.
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