IoT Attacks Detection Using Supervised Machine Learning Techniques

https://doi.org/10.28991/HIJ-2024-05-03-01
Supervised IoT Security Cyberattacks IoT Attacks.

Authors

  • Malak Aljabri Department of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah 21955,, Saudi Arabia
  • Afrah Shaahid
    2190009057@iau.edu.sa
    College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441,, Saudi Arabia https://orcid.org/0000-0002-2427-6015
  • Fatima Alnasser College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441,, Saudi Arabia
  • Asalah Saleh College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441,, Saudi Arabia
  • Dorieh Alomari College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441,, Saudi Arabia
  • Menna Aboulnour College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441,, Saudi Arabia
  • Walla Al-Eidarous Department of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah 21955,, Saudi Arabia
  • Areej Althubaity Depatment of Cybersecurity, College of Computing, Umm Al-Qura University, Makkah 21955,, Saudi Arabia
Vol. 5 No. 3 (2024): September
Research Articles

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In recent times, the growing significance of Internet of Things (IoT) devices in people's lives is undeniable, driven by their myriad benefits. However, these devices confront cybersecurity threats akin to traditional network devices, as they depend on networks for connectivity and synchronization. Artificial Intelligence (AI) techniques, specifically Machine Learning (ML) and Deep Learning (DL), have demonstrated notable reliability in the field of cyberattack detection. This study focuses on detecting Flood and Brute Force cyberattacks using Machine Learning (ML) and Deep Learning (DL) models. The primary emphasis lies in identifying traffic features that significantly detect these types of attacks. The experimental study incorporates eight models: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Gradient Boosting (GB), Naí¯ve Bayes (NB), and Artificial Neural Network (ANN). Two sets of experiments were conducted, with the first set involving six features and the subsequent set, after feature selection, focusing on a reduced set of three features. The evaluation of the proposed model's efficiency and performance relied on metrics such as Accuracy, Precision, Recall, and F1-score. Remarkably, all proposed models exhibited high performance in both sets of experiments. However, the Gradient Boosting (GB) classifier suppressed others, attaining an impressive accuracy level of 95.94% and 95.28% in the sets with six features and three features, respectively.

 

Doi: 10.28991/HIJ-2024-05-03-01

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