IoT Real-Time Attacks Classification Framework Using Machine Learning

Nader Karmous, Mohamed Ould-Elhassen Aoueileyine, Manel Abdelkader, Néji Youssef

202214 citationsDOI

Abstract

This paper presents a proposed artificial intelligence (AI) framework used to detect attacks in an IoT ecosystem. The proposed framework is an intrusion detection system (IDS) using a machine learning strategy to monitor the IoT system and detect malicious and suspicious activity. In this work, we used supervised machine learning (ML) method to increase detection accuracy and minimize data processing time. Four classification algorithms are used to evaluate the proposed model, namely Random Forest (RF), Support Vector Machines (SVM), k Nearest Neighbors (kNN) and Gaussian Naïve Bayes (GNB) algorithm. Experimental results showed that KNN performs best with 97.5% accuracy and fastest training times with 0.03 seconds (i.e. the training times are the CPU time to build the model). At the end, we proposed an implementation of the proposed IDS in a real IoT environment.

Topics & Concepts

Computer scienceNaive Bayes classifierSupport vector machineRandom forestArtificial intelligenceMachine learningIntrusion detection systemInternet of Thingsk-nearest neighbors algorithmStatistical classificationData miningEmbedded systemNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesAnomaly Detection Techniques and Applications