A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats
Anomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring. In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors,...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10415174/ |
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| author | Dheyaaldin Alsalman |
| author_facet | Dheyaaldin Alsalman |
| author_sort | Dheyaaldin Alsalman |
| collection | DOAJ |
| description | Anomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring. In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. FusionNet’s architecture leverages the diversity of these algorithms to achieve high accuracy and precision. We evaluate FusionNet’s performance on two distinct datasets, Dataset 1 and Dataset 2, and compare it with traditional machine learning models, including SVM, KNN, and RF. The results demonstrate that FusionNet consistently outperforms these models across both datasets in terms of accuracy, precision, recall, and F1 score. On Dataset 1, FusionNet achieves an accuracy of 98.5% and on Dataset 2, it attains an accuracy of 99.5%. FusionNet’s remarkable ability to detect anomalies with exceptional accuracy underscores its potential for real-world applications. This study highlights the significance of FusionNet as a robust model for anomaly detection and provides insights into its superior performance over traditional models. The results emphasize the promising prospects of FusionNet in security, healthcare, and other domains where accurate anomaly detection is crucial. |
| format | Article |
| id | doaj-art-52cf265d8e9c4279bb5505e4914600c3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-52cf265d8e9c4279bb5505e4914600c32025-01-28T00:00:48ZengIEEEIEEE Access2169-35362024-01-0112147191473010.1109/ACCESS.2024.335903310415174A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT ThreatsDheyaaldin Alsalman0https://orcid.org/0000-0002-8493-2758Department of Cybersecurity, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah, Saudi ArabiaAnomaly detection is a critical aspect of various applications, including security, healthcare, and network monitoring. In this study, we introduce FusionNet, an innovative ensemble model that combines the strengths of multiple machine learning algorithms, namely Random Forest, K-Nearest Neighbors, Support Vector Machine, and Multi-Layer Perceptron, for enhanced anomaly detection. FusionNet’s architecture leverages the diversity of these algorithms to achieve high accuracy and precision. We evaluate FusionNet’s performance on two distinct datasets, Dataset 1 and Dataset 2, and compare it with traditional machine learning models, including SVM, KNN, and RF. The results demonstrate that FusionNet consistently outperforms these models across both datasets in terms of accuracy, precision, recall, and F1 score. On Dataset 1, FusionNet achieves an accuracy of 98.5% and on Dataset 2, it attains an accuracy of 99.5%. FusionNet’s remarkable ability to detect anomalies with exceptional accuracy underscores its potential for real-world applications. This study highlights the significance of FusionNet as a robust model for anomaly detection and provides insights into its superior performance over traditional models. The results emphasize the promising prospects of FusionNet in security, healthcare, and other domains where accurate anomaly detection is crucial.https://ieeexplore.ieee.org/document/10415174/CybersecurityIoMTintrusion detectionmachine learningadaptive learningSVM |
| spellingShingle | Dheyaaldin Alsalman A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats IEEE Access Cybersecurity IoMT intrusion detection machine learning adaptive learning SVM |
| title | A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats |
| title_full | A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats |
| title_fullStr | A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats |
| title_full_unstemmed | A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats |
| title_short | A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats |
| title_sort | comparative study of anomaly detection techniques for iot security using adaptive machine learning for iot threats |
| topic | Cybersecurity IoMT intrusion detection machine learning adaptive learning SVM |
| url | https://ieeexplore.ieee.org/document/10415174/ |
| work_keys_str_mv | AT dheyaaldinalsalman acomparativestudyofanomalydetectiontechniquesforiotsecurityusingadaptivemachinelearningforiotthreats AT dheyaaldinalsalman comparativestudyofanomalydetectiontechniquesforiotsecurityusingadaptivemachinelearningforiotthreats |