HCAP: Hybrid cyber attack prediction model for securing healthcare applications.
The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of cybersecurity attac...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0321941 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850265845900509184 |
|---|---|
| author | Mohanad Faeq Ali Mohammed Shakir Mohmood Ban Salman Shukur Rex Bacarra Jamil Abedalrahim Jamil Alsayaydeh Masrullizam Mat Ibrahim Safarudin Gazali Herawan |
| author_facet | Mohanad Faeq Ali Mohammed Shakir Mohmood Ban Salman Shukur Rex Bacarra Jamil Abedalrahim Jamil Alsayaydeh Masrullizam Mat Ibrahim Safarudin Gazali Herawan |
| author_sort | Mohanad Faeq Ali |
| collection | DOAJ |
| description | The rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of cybersecurity attacks. These attacks threaten the confidentiality, integrity, and availability of sensitive healthcare data, raising concerns about the reliability of IoMT infrastructure. Addressing these challenges requires advanced cybersecurity measures to protect the dynamic IoMT ecosystem from evolving threats. This research focuses on enhancing cyberattack prediction and prevention in IoMT environments through innovative Machine-learning techniques to improve healthcare data security and resilience. However, the existing model's efficiency depends on the diversity of data, which leads to computational complexity issues. In addition, the conventional model faces overfitting issues in training data, which causes prediction inaccuracies. Thus, the research introduces the hybridized cyber attack prediction model (HCAP) and analyzes various IoMT data source information to address the limitations of dataset availability issues. The gathered information is processed with the help of Principal Component-Recursive Feature Elimination (PC-RFE), which eliminates the irrelevant features. The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model's ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. The results demonstrated that the proposed HCAP model achieved 98% accuracy in detecting cyberattacks and outperformed existing models, reducing the false positive rate by 25%. The false negative rate by 20% and a 30% improvement in computational efficiency enhances the reliability of IoMT threat detection in healthcare applications. |
| format | Article |
| id | doaj-art-1d8d0ea3266a4c38bb9227a4dd4f12e0 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1d8d0ea3266a4c38bb9227a4dd4f12e02025-08-20T01:54:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032194110.1371/journal.pone.0321941HCAP: Hybrid cyber attack prediction model for securing healthcare applications.Mohanad Faeq AliMohammed Shakir MohmoodBan Salman ShukurRex BacarraJamil Abedalrahim Jamil AlsayaydehMasrullizam Mat IbrahimSafarudin Gazali HerawanThe rapid development and integration of interconnected healthcare devices and communication networks within the Internet of Medical Things (IoMT) have significantly enhanced healthcare services. However, this growth has also introduced new vulnerabilities, increasing the risk of cybersecurity attacks. These attacks threaten the confidentiality, integrity, and availability of sensitive healthcare data, raising concerns about the reliability of IoMT infrastructure. Addressing these challenges requires advanced cybersecurity measures to protect the dynamic IoMT ecosystem from evolving threats. This research focuses on enhancing cyberattack prediction and prevention in IoMT environments through innovative Machine-learning techniques to improve healthcare data security and resilience. However, the existing model's efficiency depends on the diversity of data, which leads to computational complexity issues. In addition, the conventional model faces overfitting issues in training data, which causes prediction inaccuracies. Thus, the research introduces the hybridized cyber attack prediction model (HCAP) and analyzes various IoMT data source information to address the limitations of dataset availability issues. The gathered information is processed with the help of Principal Component-Recursive Feature Elimination (PC-RFE), which eliminates the irrelevant features. The extracted features are fed into the lion-optimization technique to fine-tune the hyperparameters of the recurrent neural networks, enhancing the model's ability to efficiently predict cybersecurity threats with a maximum recognition rate in IoMT environments. The recurrent networks, specifically Long Short Term Memory (LSTM), process data from healthcare devices, identifying abnormal patterns that indicate potential cyberattacks over time. The created system was implemented using Python, and various metrics, including false positive and false negative rates, accuracy, precision, recall, and computational efficiency, were used for evaluation. The results demonstrated that the proposed HCAP model achieved 98% accuracy in detecting cyberattacks and outperformed existing models, reducing the false positive rate by 25%. The false negative rate by 20% and a 30% improvement in computational efficiency enhances the reliability of IoMT threat detection in healthcare applications.https://doi.org/10.1371/journal.pone.0321941 |
| spellingShingle | Mohanad Faeq Ali Mohammed Shakir Mohmood Ban Salman Shukur Rex Bacarra Jamil Abedalrahim Jamil Alsayaydeh Masrullizam Mat Ibrahim Safarudin Gazali Herawan HCAP: Hybrid cyber attack prediction model for securing healthcare applications. PLoS ONE |
| title | HCAP: Hybrid cyber attack prediction model for securing healthcare applications. |
| title_full | HCAP: Hybrid cyber attack prediction model for securing healthcare applications. |
| title_fullStr | HCAP: Hybrid cyber attack prediction model for securing healthcare applications. |
| title_full_unstemmed | HCAP: Hybrid cyber attack prediction model for securing healthcare applications. |
| title_short | HCAP: Hybrid cyber attack prediction model for securing healthcare applications. |
| title_sort | hcap hybrid cyber attack prediction model for securing healthcare applications |
| url | https://doi.org/10.1371/journal.pone.0321941 |
| work_keys_str_mv | AT mohanadfaeqali hcaphybridcyberattackpredictionmodelforsecuringhealthcareapplications AT mohammedshakirmohmood hcaphybridcyberattackpredictionmodelforsecuringhealthcareapplications AT bansalmanshukur hcaphybridcyberattackpredictionmodelforsecuringhealthcareapplications AT rexbacarra hcaphybridcyberattackpredictionmodelforsecuringhealthcareapplications AT jamilabedalrahimjamilalsayaydeh hcaphybridcyberattackpredictionmodelforsecuringhealthcareapplications AT masrullizammatibrahim hcaphybridcyberattackpredictionmodelforsecuringhealthcareapplications AT safarudingazaliherawan hcaphybridcyberattackpredictionmodelforsecuringhealthcareapplications |