Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers
Cyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection syst...
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MDPI AG
2025-02-01
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| author | Jawad Ashraf Ghulam Musa Raza Byung-Seo Kim Abdul Wahid Hye-Young Kim |
| author_facet | Jawad Ashraf Ghulam Musa Raza Byung-Seo Kim Abdul Wahid Hye-Young Kim |
| author_sort | Jawad Ashraf |
| collection | DOAJ |
| description | Cyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection system (IDS) capable of identifying various modern and traditional attacks with high accuracy. Most existing machine-learning-based intrusion-detection systems for IoT have been trained using outdated datasets that do not accurately reflect IoT scenarios. Additionally, current research does not adequately address which machine-learning classifiers are most suitable for developing an efficient IDS in IoT environments. In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, and Artificial Neural Networks. Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT–IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. The second-best performer is Naïve Bayes, with an accuracy of 98.8%. |
| format | Article |
| id | doaj-art-2e70e4d499f44cdba7ef6603ca631680 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2e70e4d499f44cdba7ef6603ca6316802025-08-20T02:44:52ZengMDPI AGApplied Sciences2076-34172025-02-01154204310.3390/app15042043Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning ClassifiersJawad Ashraf0Ghulam Musa Raza1Byung-Seo Kim2Abdul Wahid3Hye-Young Kim4School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Software and Communications Engineering, Hongik University, Sejong 30016, Republic of KoreaDepartment of Software and Communications Engineering, Hongik University, Sejong 30016, Republic of KoreaSchool of Computer Science, University of Birmingham, Birmingham B15 2TT, UKDepartment of Games, School of Games, Hongik University, Sejong 30016, Republic of KoreaCyber-attacks have become a significant concern today, particularly in IoT environments where security poses a substantial challenge due to the distributed nature and heterogeneity of protocols. To efficiently detect threats in IoT networks, it is crucial to develop a robust intrusion-detection system (IDS) capable of identifying various modern and traditional attacks with high accuracy. Most existing machine-learning-based intrusion-detection systems for IoT have been trained using outdated datasets that do not accurately reflect IoT scenarios. Additionally, current research does not adequately address which machine-learning classifiers are most suitable for developing an efficient IDS in IoT environments. In our research, we have developed and trained a real-time intrusion-detection system for IoT networks that can detect multiple modern and traditional threats with high accuracy. We created seven instances of real-time IDS using state-of-the-art machine-learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, Naïve Bayes, and Artificial Neural Networks. Using the Pearson Correlation Coefficient, we extracted the most relevant features from the BoT–IoT dataset. After rigorous preprocessing, we used these data to train our algorithms. Our trained model, INIDS, is not only up to date and real-time but also capable of accurately identifying multiple categories of attacks specifically related to IoT networks. To achieve maximum accuracy, instead of selecting only one classifier, we evaluated seven advanced machine-learning algorithms and provided a comprehensive comparison of their performance and efficiency in the context of IoT networks. This analysis can guide future researchers in choosing the right machine-learning algorithms for developing IDS. We found that Random Forest is the most robust classifier for IoT-based network intrusion-detection systems, achieving an accuracy of 99.2%. The second-best performer is Naïve Bayes, with an accuracy of 98.8%.https://www.mdpi.com/2076-3417/15/4/2043intrusion detectionanomaly detectionIoTmachine learningcyber-attacksnetwork security |
| spellingShingle | Jawad Ashraf Ghulam Musa Raza Byung-Seo Kim Abdul Wahid Hye-Young Kim Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers Applied Sciences intrusion detection anomaly detection IoT machine learning cyber-attacks network security |
| title | Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers |
| title_full | Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers |
| title_fullStr | Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers |
| title_full_unstemmed | Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers |
| title_short | Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers |
| title_sort | making a real time iot network intrusion detection system inids using a realistic bot iot dataset with multiple machine learning classifiers |
| topic | intrusion detection anomaly detection IoT machine learning cyber-attacks network security |
| url | https://www.mdpi.com/2076-3417/15/4/2043 |
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