SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization
The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/17/3541 |
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| author | Nuruzzaman Faruqui Mohammad Abu Yousuf Md Whaiduzzaman AKM Azad Salem A. Alyami Pietro Liò Muhammad Ashad Kabir Mohammad Ali Moni |
| author_facet | Nuruzzaman Faruqui Mohammad Abu Yousuf Md Whaiduzzaman AKM Azad Salem A. Alyami Pietro Liò Muhammad Ashad Kabir Mohammad Ali Moni |
| author_sort | Nuruzzaman Faruqui |
| collection | DOAJ |
| description | The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion. |
| first_indexed | 2024-03-10T23:26:08Z |
| format | Article |
| id | doaj.art-c33c8fb91eef4bdf9f3136bd024b1539 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:26:08Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-c33c8fb91eef4bdf9f3136bd024b15392023-11-19T08:00:39ZengMDPI AGElectronics2079-92922023-08-011217354110.3390/electronics12173541SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM HybridizationNuruzzaman Faruqui0Mohammad Abu Yousuf1Md Whaiduzzaman2AKM Azad3Salem A. Alyami4Pietro Liò5Muhammad Ashad Kabir6Mohammad Ali Moni7Department of Software Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, BangladeshInstitute of Information Technology, Jahangirnagar University, Savar, Dhaka 1342, BangladeshFaculty of Science, School of Information Systems, Queensland University of Technology, 2 George St., Brisbane, QLD 4000, AustraliaDepartment of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi ArabiaDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi ArabiaDepartment of Computer Science and Technology, The University of Cambridge, Cambridge CB2 1TN, UKSchool of Computing, Mathematics, and Engineering, Charles Sturt University, Bathurst, NSW 2795, AustraliaArtificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, Brisbane, QLD 4072, AustraliaThe Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a competitive price in the market. As a result, IoMTs cannot employ advanced security algorithms to defend against cyber-attacks. IoMT has become easy prey for cybercriminals due to its access to valuable data and the rapidly expanding market, as well as being comparatively easier to exploit.As a result, the intrusion rate in IoMT is experiencing a surge. This paper proposes a novel Intrusion Detection System (IDS), namely SafetyMed, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to defend against intrusion from sequential and grid data. SafetyMed is the first IDS that protects IoMT devices from malicious image data and sequential network traffic. This innovative IDS ensures an optimized detection rate by trade-off between False Positive Rate (FPR) and Detection Rate (DR). It detects intrusions with an average accuracy of 97.63% with average precision and recall, and has an F1-score of 98.47%, 97%, and 97.73%, respectively. In summary, SafetyMed has the potential to revolutionize many vulnerable sectors (e.g., medical) by ensuring maximum protection against IoMT intrusion.https://www.mdpi.com/2079-9292/12/17/3541internet of medical thingsintrusion detection systemconvolutional neural networklong short-term memoryresponse mechanismIoMT |
| spellingShingle | Nuruzzaman Faruqui Mohammad Abu Yousuf Md Whaiduzzaman AKM Azad Salem A. Alyami Pietro Liò Muhammad Ashad Kabir Mohammad Ali Moni SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization Electronics internet of medical things intrusion detection system convolutional neural network long short-term memory response mechanism IoMT |
| title | SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization |
| title_full | SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization |
| title_fullStr | SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization |
| title_full_unstemmed | SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization |
| title_short | SafetyMed: A Novel IoMT Intrusion Detection System Using CNN-LSTM Hybridization |
| title_sort | safetymed a novel iomt intrusion detection system using cnn lstm hybridization |
| topic | internet of medical things intrusion detection system convolutional neural network long short-term memory response mechanism IoMT |
| url | https://www.mdpi.com/2079-9292/12/17/3541 |
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