An Intrusion Detection Model using election-Based Feature Selection and K-NN

Abstract

The Internet of Things (IoT) is a harmonized embedded object and sensor set. It is a target of intrusions, which leads to considering the security of the IoT environment. Considering their vital role in network security, intrusion detection systems (IDS) have received significant interest in the research community. IDS can be divided into anomaly intrusion detection systems (AIDS) and signature intrusion detection systems (SIDS). In this work, we integrate AIDS to secure IoT environments better and enhance the privacy of data circulated within sensors and actuators which interact with the real world. However, a network intrusion detection system (NIDS) mitigates security issues. We present in this paper an effective NIDS for IoT security relying on machine learning such as K-NN and feature selection. Specifically, we designed our model using K-Nearest Neighbors (K-NN) algorithm to boost the performance and enhance the IDS accuracy (ACC), with feature selection algorithms separately, such as principal component analysis (PCA), genetic algorithm (GA) and univariate statistical test. We elected every feature that two or three methods have selected. Hence, our election-based feature selection method lasted with five features where the model has shown promising results in ACC, false alarm rate (FAR), and detection rate (DR). To evaluate the performance of our model on imbalanced datasets, we ran our tests on the Bot-IoT dataset. We measured the model using Matthew's Correlation Coefficient (MCC) to have a better performance evaluation, where it scored up to 97%. The suggested model resulted in 99.99% ACC and considerably changed the training time from 21696 seconds for the entire dataset to 102 seconds for the five selected features. Our proposed model provides significant performances compared with previous models