An efficient algorithmic technique for feature selection in IoT based intrusion detection system

Alok Kumar Pani

doi:10.17485/ijst/v14i1.2057

Abstract

Background/Objectives Internet of Things (IoT) is an emerging technology that involves in monitoring the environment and the IoT networks are most vulnerable to attacks due to various number of devices connected in the network. The Intrusion detection technique has been applied to analyze the anomaly in the network. The Existing models have the limitation of inefficiency in the intrusion detection due to the overfit in the models. Methods/Statisticalanalysis: In this research, the Flower Pollination Algorithm (FPA) has been applied in the intrusion detection method to increase the efficiency of the IoT network. The FPA method has the advantage of long distance pollination and flower consistency to analyze the features effectively. The FPA selects the features in the IoT network and apply the features for the classifier to detect the attacks. The classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) are used to detect the intrusions in the network. Findings: This experimental result shows that the proposed FPA method with ANN has the accuracy of 99.5 % in detection and existing ANN has 99.4 % accuracy in detection. Novelty/Applications: The FPA method has the advantages of long distance pollination and flower consistency which helps to analyze the network features effectively. Keywords: Artificial neural network; flower pollination algorithm; internet of things; intrusion detection; long distance pollination

Highlights

The embedded devices are connected to the Internet, where the devices can be remotely accessed and used for monitoring refers to the Internet of Things (IoT) paradigm[1]
To overcome the limitation of the existing method, the Flower Pollination Algorithm (FPA) method is proposed to increase the performance of the Intrusion detection in IoT
The security in IoT environment is low because of the vast number of devices in the IoT network and the data can be accessed from a single node

Summary

Introduction

The embedded devices are connected to the Internet, where the devices can be remotely accessed and used for monitoring refers to the Internet of Things (IoT) paradigm[1]. The era of the internet gives rise to smart devices and automated the task and thousands of users are connected to the internet to get the benefits of the promising IoT solutions [2]. These applications include the health care system, home automation, smart grids and smart cities [3]. The hacker may take advantages of the IoT devices, which is a threatening to privacy and security of the user For example, the Denial-of-Services (DDoS) attacks affects the IoT devices and provide the information to the hackers[5]

Objectives

Methods

Findings

Conclusion