IoT intrusion detection system based on machine learning and deep learning
The proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML an...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
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University of Information Technology and Communications
2025-06-01
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| Series: | Iraqi Journal for Computers and Informatics |
| Subjects: | |
| Online Access: | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/552 |
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| _version_ | 1850138678474571776 |
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| author | karrar Majid Jasim Joolan Rokan Nayef |
| author_facet | karrar Majid Jasim Joolan Rokan Nayef |
| author_sort | karrar Majid Jasim |
| collection | DOAJ |
| description | The proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML and Deep Learning DL models on two benchmark datasets, BoT-IoT and CIC-IDS2017, to develop efficient IDS. Among ML models, XGBoost demonstrated the best performance, achieving 99.99% accuracy on BoT-IoT and 99.91% on CIC-IDS2017 with superior computational efficiency. For DL, Convolutional Neural Networks CNNs achieved 99.99% accuracy on BoT-IoT and 99.61% on CIC-IDS2017 with preprocessing, highlighting the critical role of data preparation. These findings underline the effectiveness of advanced ML/DL models and preprocessing techniques in enhancing IoT security, providing a pathway for real-time, scalable intrusion detection in IoT environments. |
| format | Article |
| id | doaj-art-c8e06fba898d4b71bf2cdf26e7089a5a |
| institution | OA Journals |
| issn | 2313-190X 2520-4912 |
| language | Arabic |
| publishDate | 2025-06-01 |
| publisher | University of Information Technology and Communications |
| record_format | Article |
| series | Iraqi Journal for Computers and Informatics |
| spelling | doaj-art-c8e06fba898d4b71bf2cdf26e7089a5a2025-08-20T02:30:31ZaraUniversity of Information Technology and CommunicationsIraqi Journal for Computers and Informatics2313-190X2520-49122025-06-01511839310.25195/ijci.v51i1.552515IoT intrusion detection system based on machine learning and deep learningkarrar Majid Jasim0Joolan Rokan Nayef1University of Information Technology and CommunicationsUniversity of Information Technology and CommunicationsThe proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML and Deep Learning DL models on two benchmark datasets, BoT-IoT and CIC-IDS2017, to develop efficient IDS. Among ML models, XGBoost demonstrated the best performance, achieving 99.99% accuracy on BoT-IoT and 99.91% on CIC-IDS2017 with superior computational efficiency. For DL, Convolutional Neural Networks CNNs achieved 99.99% accuracy on BoT-IoT and 99.61% on CIC-IDS2017 with preprocessing, highlighting the critical role of data preparation. These findings underline the effectiveness of advanced ML/DL models and preprocessing techniques in enhancing IoT security, providing a pathway for real-time, scalable intrusion detection in IoT environments.https://ijci.uoitc.edu.iq/index.php/ijci/article/view/552iot securitydeep learningmachine learning,intrusion detection |
| spellingShingle | karrar Majid Jasim Joolan Rokan Nayef IoT intrusion detection system based on machine learning and deep learning Iraqi Journal for Computers and Informatics iot security deep learning machine learning, intrusion detection |
| title | IoT intrusion detection system based on machine learning and deep learning |
| title_full | IoT intrusion detection system based on machine learning and deep learning |
| title_fullStr | IoT intrusion detection system based on machine learning and deep learning |
| title_full_unstemmed | IoT intrusion detection system based on machine learning and deep learning |
| title_short | IoT intrusion detection system based on machine learning and deep learning |
| title_sort | iot intrusion detection system based on machine learning and deep learning |
| topic | iot security deep learning machine learning, intrusion detection |
| url | https://ijci.uoitc.edu.iq/index.php/ijci/article/view/552 |
| work_keys_str_mv | AT karrarmajidjasim iotintrusiondetectionsystembasedonmachinelearninganddeeplearning AT joolanrokannayef iotintrusiondetectionsystembasedonmachinelearninganddeeplearning |