L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT
The rapid growth of IoT has significantly changed modern technology by allowing devices, systems, and services to connect easily across different areas. Due to the growing popularity of Internet of Things (IoT) devices, attackers focus more and more on finding new methods, ways, and vulnerabilities...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10830526/ |
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| author | Gokhan Akar Shaaban Sahmoud Mustafa Onat Unal Cavusoglu Emmanuel Malondo |
| author_facet | Gokhan Akar Shaaban Sahmoud Mustafa Onat Unal Cavusoglu Emmanuel Malondo |
| author_sort | Gokhan Akar |
| collection | DOAJ |
| description | The rapid growth of IoT has significantly changed modern technology by allowing devices, systems, and services to connect easily across different areas. Due to the growing popularity of Internet of Things (IoT) devices, attackers focus more and more on finding new methods, ways, and vulnerabilities to penetrate IoT networks. Although IoT devices are utilized across a wide range of domains, the Internet of Medical Things (IoMT) holds particular significance due to the sensitive and critical nature of medical information. Consequently, the security of these devices must be treated as a paramount concern within the IoT landscape. In this paper, we propose a novel approach for detecting various intrusion attacks targeting Internet of Medical Things (IoMT) devices, utilizing an enhanced version of the LSTM deep learning algorithm. To evaluate and compare the proposed algorithm with other methods, we used the CICIoMT2024 dataset, which encompasses various types of equipment and corresponding attacks. The results demonstrate that the proposed novel approach achieved an accuracy of 98% for 19 classes, which is remarkably high for classifications and presents a significant and promising outcome for IoMT environments. |
| format | Article |
| id | doaj-art-bcd49c9a9c584dae8ebc50b517b4c7b3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bcd49c9a9c584dae8ebc50b517b4c7b32025-01-15T00:02:34ZengIEEEIEEE Access2169-35362025-01-01137002701310.1109/ACCESS.2025.352688310830526L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMTGokhan Akar0https://orcid.org/0000-0001-8592-4146Shaaban Sahmoud1https://orcid.org/0000-0003-0148-2382Mustafa Onat2https://orcid.org/0000-0003-4304-3361Unal Cavusoglu3Emmanuel Malondo4Department of Electrical and Electronics Faculty (Engineering), Marmara University, İstanbul, TürkiyeEngineering Faculty, Fatih Sultan Mehmet Vakif University, İstanbul, TürkiyeDepartment of Electrical and Electronics Faculty (Engineering), Marmara University, İstanbul, TürkiyeComputer Sciences Faculty, Sakarya University, Sakarya, TürkiyeEngineering Faculty, CESI University, Rouen, FranceThe rapid growth of IoT has significantly changed modern technology by allowing devices, systems, and services to connect easily across different areas. Due to the growing popularity of Internet of Things (IoT) devices, attackers focus more and more on finding new methods, ways, and vulnerabilities to penetrate IoT networks. Although IoT devices are utilized across a wide range of domains, the Internet of Medical Things (IoMT) holds particular significance due to the sensitive and critical nature of medical information. Consequently, the security of these devices must be treated as a paramount concern within the IoT landscape. In this paper, we propose a novel approach for detecting various intrusion attacks targeting Internet of Medical Things (IoMT) devices, utilizing an enhanced version of the LSTM deep learning algorithm. To evaluate and compare the proposed algorithm with other methods, we used the CICIoMT2024 dataset, which encompasses various types of equipment and corresponding attacks. The results demonstrate that the proposed novel approach achieved an accuracy of 98% for 19 classes, which is remarkably high for classifications and presents a significant and promising outcome for IoMT environments.https://ieeexplore.ieee.org/document/10830526/Internet of Medical Things (IoMT)intrusion detection systemInternet of Things Securitysecurity of healthcare systems |
| spellingShingle | Gokhan Akar Shaaban Sahmoud Mustafa Onat Unal Cavusoglu Emmanuel Malondo L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT IEEE Access Internet of Medical Things (IoMT) intrusion detection system Internet of Things Security security of healthcare systems |
| title | L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT |
| title_full | L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT |
| title_fullStr | L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT |
| title_full_unstemmed | L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT |
| title_short | L2D2: A Novel LSTM Model for Multi-Class Intrusion Detection Systems in the Era of IoMT |
| title_sort | l2d2 a novel lstm model for multi class intrusion detection systems in the era of iomt |
| topic | Internet of Medical Things (IoMT) intrusion detection system Internet of Things Security security of healthcare systems |
| url | https://ieeexplore.ieee.org/document/10830526/ |
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