An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Sensors

Fawad Masood¹  Fehaid Alqahtani²  Sadaqat ur Rehman³  Boubakr Nour⁴  WilliamJ. Buchanan⁵  Mandar Gogate⁵  Jawad Ahmad⁵  Andrew Churcher⁵  Rehmat Ullah⁶ 

[1] College of Information Engineering, Yangzhou University, Yangzhou 225127, China;Department of Computer Science, King Fahad Naval Academy, Al Jubail 35512, Saudi Arabia;Department of Computer Science, Namal Institute, Mianwali 42250, Pakistan;School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China;School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK;School of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast BT9 5BN, UK;

关键词: Internet of Things (IoT);    IoT attacks;    security;    intrusion detection systems;    privacy;    machine learning;   

DOI  :  10.3390/s21020446

来源: DOAJ

【 摘 要 】

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

【 授权许可】

Unknown