Research Area:  Machine Learning

Abstract:

The Internet of Things (IoT) is vulnerable to various attacks, due to the presence of tiny computing devices. To enhance the security of the IoT, this paper builds a lightweight intrusion detection system (IDS) based on two machine learning techniques, namely, feature selection and feature classification. The feature selection was realized by the filter-based method, thanks to its relatively low computing cost. The feature classification algorithm for our system was identified through comparison between logistic regression (LR), naive Bayes (NB), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM) and multilayer perceptron (MLP). Finally, the DT algorithm was selected for our system, owing to its outstanding performance on several datasets. The research results provide a guide on choosing the optimal feature selection method for machine learning.

Keywords:  
Internet of Things (IoT)
Intrusion detection system (IDS)
Anomaly detection
Feature selection

Author(s) Name:  Samir Fenanir1, Fouzi Semchedine, Abderrahmane Baadache

Publisher name:  IIETA

DOI:  10.18280/ria.330306

Volume Information:  Volume 33

Paper Link:   https://www.researchgate.net/profile/Samir_Fenanir2/publication/337453353_A_Machine_Learning-Based_Lightweight_Intrusion_Detection_System_for_the_Internet_of_Things/links/5dda84b5a6fdccdb44609b91/A-Machine-Learning-Based-Lightweight-Intrusion-Detection-System-for-the-Internet-of-Things.pdf