Automated Network Intrusion Detection for Internet of Things: Security Enhancements
Security attacks are becoming more sophisticated and common as connected devices rapidly exchange personal, sensitive, and important data. Security solutions are therefore required for Internet of Things (IoT) environments. System administrators receive alerts through an automatic Network Intrusion...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10444102/ |
| _version_ | 1797272712962375680 |
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| author | Louai A. Maghrabi |
| author_facet | Louai A. Maghrabi |
| author_sort | Louai A. Maghrabi |
| collection | DOAJ |
| description | Security attacks are becoming more sophisticated and common as connected devices rapidly exchange personal, sensitive, and important data. Security solutions are therefore required for Internet of Things (IoT) environments. System administrators receive alerts through an automatic Network Intrusion Detection (NID) system when security breaches occur. An automatic NID can be an effective tool to protect IoT networks against various attacks. It is possible to detect intrusions using a variety of intrusion detection techniques, but the performance and class imbalance in the dataset make this a difficult process. To improve detection rates and decrease false alarms, intrusion detection accuracy must be improved. In this paper, an automatic NID system is proposed leveraging a renowned machine learning model named Random Forest (RF) on the (UNSW-NB15) dataset collected from Kaggle. The experimental results indicate that the proposed model not only has higher accuracy at 90.17% surpassing the baseline approach by 7.34%, but also has precision, recall, and F1 scores up to 90.14%, 90.17, and 90.14%, respectively. Moreover, 98.83% accuracy is achieved with a balanced class dataset by using random resampling techniques to generate synthetic data of minority attacks. |
| first_indexed | 2024-03-07T14:33:19Z |
| format | Article |
| id | doaj.art-c69cb33669764e88b83ca90901c18417 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T14:33:19Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c69cb33669764e88b83ca90901c184172024-03-06T00:00:58ZengIEEEIEEE Access2169-35362024-01-0112308393085110.1109/ACCESS.2024.336923710444102Automated Network Intrusion Detection for Internet of Things: Security EnhancementsLouai A. Maghrabi0https://orcid.org/0000-0001-8513-0645Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi ArabiaSecurity attacks are becoming more sophisticated and common as connected devices rapidly exchange personal, sensitive, and important data. Security solutions are therefore required for Internet of Things (IoT) environments. System administrators receive alerts through an automatic Network Intrusion Detection (NID) system when security breaches occur. An automatic NID can be an effective tool to protect IoT networks against various attacks. It is possible to detect intrusions using a variety of intrusion detection techniques, but the performance and class imbalance in the dataset make this a difficult process. To improve detection rates and decrease false alarms, intrusion detection accuracy must be improved. In this paper, an automatic NID system is proposed leveraging a renowned machine learning model named Random Forest (RF) on the (UNSW-NB15) dataset collected from Kaggle. The experimental results indicate that the proposed model not only has higher accuracy at 90.17% surpassing the baseline approach by 7.34%, but also has precision, recall, and F1 scores up to 90.14%, 90.17, and 90.14%, respectively. Moreover, 98.83% accuracy is achieved with a balanced class dataset by using random resampling techniques to generate synthetic data of minority attacks.https://ieeexplore.ieee.org/document/10444102/Intrusion detectionIOTBERTclassificationmachine learningrandom forest |
| spellingShingle | Louai A. Maghrabi Automated Network Intrusion Detection for Internet of Things: Security Enhancements IEEE Access Intrusion detection IOT BERT classification machine learning random forest |
| title | Automated Network Intrusion Detection for Internet of Things: Security Enhancements |
| title_full | Automated Network Intrusion Detection for Internet of Things: Security Enhancements |
| title_fullStr | Automated Network Intrusion Detection for Internet of Things: Security Enhancements |
| title_full_unstemmed | Automated Network Intrusion Detection for Internet of Things: Security Enhancements |
| title_short | Automated Network Intrusion Detection for Internet of Things: Security Enhancements |
| title_sort | automated network intrusion detection for internet of things security enhancements |
| topic | Intrusion detection IOT BERT classification machine learning random forest |
| url | https://ieeexplore.ieee.org/document/10444102/ |
| work_keys_str_mv | AT louaiamaghrabi automatednetworkintrusiondetectionforinternetofthingssecurityenhancements |