Machine Learning-Based Detection for Unauthorized Access to IoT Devices

被引:8
作者
Aljabri, Malak [1 ]
Alahmadi, Amal A. [2 ]
Mohammad, Rami Mustafa A. [3 ]
Alhaidari, Fahd [2 ]
Aboulnour, Menna [4 ]
Alomari, Dorieh M. [5 ]
Mirza, Samiha [4 ]
机构
[1] Umm Al Qura Univ, Coll Comp & Informat Syst, Dept Comp Sci, Mecca 21955, Saudi Arabia
[2] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, Dept Networks & Commun, POB 1982, Dammam 31441, Saudi Arabia
[3] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, Dept Comp Informat Syst, POB 1982, Dammam 31441, Saudi Arabia
[4] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, SAUDI ARAMCO Cybersecur Chair, Dept Comp Sci, POB 1982, Dammam 31441, Saudi Arabia
[5] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, SAUDI ARAMCO Cybersecur Chair, Dept Comp Engn, POB 1982, Dammam 31441, Saudi Arabia
关键词
internet of things; machine learning; deep learning; network security;
D O I
10.3390/jsan12020027
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet of Things (IoT) has become widely adopted in businesses, organizations, and daily lives. They are usually characterized by transferring and processing sensitive data. Attackers have exploited this prospect of IoT devices to compromise user data's integrity and confidentiality. Considering the dynamic nature of the attacks, artificial intelligence (AI)-based techniques incorporating machine learning (ML) are promising techniques for identifying such attacks. However, the dataset being utilized features engineering techniques, and the kind of classifiers play significant roles in how accurate AI-based predictions are. Therefore, for the IoT environment, there is a need to contribute more to this context by evaluating different AI-based techniques on datasets that effectively capture the environment's properties. In this paper, we evaluated various ML models with the consideration of both binary and multiclass classification models validated on a new dedicated IoT dataset. Moreover, we investigated the impact of different features engineering techniques including correlation analysis and information gain. The experimental work conducted on bagging, k-nearest neighbor (KNN), J48, random forest (RF), logistic regression (LR), and multi-layer perceptron (MLP) models revealed that RF achieved the highest performance across all experiment sets, with a receiver operating characteristic (ROC) of 99.9%.
引用
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页数:14
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