IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture
<p dir="ltr">The Internet of Medical Things (IoMT) is a resource-constrained device with limited computational capabilities. However, the market worth of this section is booming rapidly. The IoMT manufacturers need to offer their products at a competitive price, which forces them to...
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2025
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| _version_ | 1864513525496414208 |
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| author | Moutaz Alazab (17730060) |
| author2 | Albara Awajan (20083410) Areej Obeidat (22502735) Nuruzzaman Faruqui (19748945) Hafeez Ur Rehman (3506369) |
| author2_role | author author author author |
| author_facet | Moutaz Alazab (17730060) Albara Awajan (20083410) Areej Obeidat (22502735) Nuruzzaman Faruqui (19748945) Hafeez Ur Rehman (3506369) |
| author_role | author |
| dc.creator.none.fl_str_mv | Moutaz Alazab (17730060) Albara Awajan (20083410) Areej Obeidat (22502735) Nuruzzaman Faruqui (19748945) Hafeez Ur Rehman (3506369) |
| dc.date.none.fl_str_mv | 2025-08-25T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-025-11527-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/IntruSafe_a_FCNN-LSTM_hybrid_IoMT_intrusion_detection_system_for_both_string_and_2D-spatial_data_using_sandwich_architecture/30971668 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning IDS IOMT Cybersecurity LSTM FCNN |
| dc.title.none.fl_str_mv | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The Internet of Medical Things (IoMT) is a resource-constrained device with limited computational capabilities. However, the market worth of this section is booming rapidly. The IoMT manufacturers need to offer their products at a competitive price, which forces them to use simplified architecture, leaving limited and, to some extent, no scope to employ sophisticated cybersecurity algorithms. As a result, IoMT has become a lucrative practice ground for cybercriminals. The IoMT sector deals with valuable, confidential healthcare-related data and offers convenient, personalized healthcare services. That is why the market demand and IoMT intrusion are experiencing massive growth. An innovative Intrusion Detection System (IDS), IntruSafe, has been studied, developed, and presented in this paper that combines Fully Connected Convolutional Neural Network (FCNN) and Long Short-Term Memory (LSTM) to protect the IoMT network from malicious signals. The IntruSafe combines FCNN and LSTM to ensure the detection of both malicious text and image data. It detects and simultaneously protects the IoMT network from further intrusion with only a 0.18% service interruption rate. This high-performing IDS detects intrusion with 97.66% accuracy, 98.50% precision, 97.33% recall, and 97.85% F1-score. With outstanding performance, IntruSafe is a promising IDS that will facilitate further growth of the IoMT sector while minimizing the risks of a successful intrusion.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-025-11527-5" target="_blank">https://dx.doi.org/10.1007/s00521-025-11527-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_de9a8834252f6873432ab2045acacdd4 |
| identifier_str_mv | 10.1007/s00521-025-11527-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30971668 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architectureMoutaz Alazab (17730060)Albara Awajan (20083410)Areej Obeidat (22502735)Nuruzzaman Faruqui (19748945)Hafeez Ur Rehman (3506369)Health sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceCybersecurity and privacyMachine learningIDSIOMTCybersecurityLSTMFCNN<p dir="ltr">The Internet of Medical Things (IoMT) is a resource-constrained device with limited computational capabilities. However, the market worth of this section is booming rapidly. The IoMT manufacturers need to offer their products at a competitive price, which forces them to use simplified architecture, leaving limited and, to some extent, no scope to employ sophisticated cybersecurity algorithms. As a result, IoMT has become a lucrative practice ground for cybercriminals. The IoMT sector deals with valuable, confidential healthcare-related data and offers convenient, personalized healthcare services. That is why the market demand and IoMT intrusion are experiencing massive growth. An innovative Intrusion Detection System (IDS), IntruSafe, has been studied, developed, and presented in this paper that combines Fully Connected Convolutional Neural Network (FCNN) and Long Short-Term Memory (LSTM) to protect the IoMT network from malicious signals. The IntruSafe combines FCNN and LSTM to ensure the detection of both malicious text and image data. It detects and simultaneously protects the IoMT network from further intrusion with only a 0.18% service interruption rate. This high-performing IDS detects intrusion with 97.66% accuracy, 98.50% precision, 97.33% recall, and 97.85% F1-score. With outstanding performance, IntruSafe is a promising IDS that will facilitate further growth of the IoMT sector while minimizing the risks of a successful intrusion.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-025-11527-5" target="_blank">https://dx.doi.org/10.1007/s00521-025-11527-5</a></p>2025-08-25T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-025-11527-5https://figshare.com/articles/journal_contribution/IntruSafe_a_FCNN-LSTM_hybrid_IoMT_intrusion_detection_system_for_both_string_and_2D-spatial_data_using_sandwich_architecture/30971668CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/309716682025-08-25T09:00:00Z |
| spellingShingle | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture Moutaz Alazab (17730060) Health sciences Health services and systems Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning IDS IOMT Cybersecurity LSTM FCNN |
| status_str | publishedVersion |
| title | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture |
| title_full | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture |
| title_fullStr | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture |
| title_full_unstemmed | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture |
| title_short | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture |
| title_sort | IntruSafe: a FCNN-LSTM hybrid IoMT intrusion detection system for both string and 2D-spatial data using sandwich architecture |
| topic | Health sciences Health services and systems Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning IDS IOMT Cybersecurity LSTM FCNN |