Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

International Journal of Environmental Research and Public Health

卷:17

MohammadMehedi Hassan¹  Tasadduq Imam²  MdMamunur Rashid³  Steven Gordon³  Joarder Kamruzzaman⁴ 

[1] Information Systems Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia;

[2] School of Business and Law, CQUniversity, Melbourne Campus, VIC 3000, Australia;

[3] School of Engineering and Technology, CQUniversity, Rockhampton North, QLD 4701, Australia;

[4] School of Engineering, Information Technology and Physical Sciences, Federation University Australia, Gippsland Campus, VIC 3842, Australia;

关键词: smart city;    Internet of Things;    cybersecurity;    anomaly detection;    machine learning;   

DOI  :  10.3390/ijerph17249347

来源: DOAJ

【 摘 要 】

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

【 授权许可】

Unknown