A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments
Source abstract
Abstract
The increasing use of IoHT devices in healthcare has brought about revolutionary advancements, but it has also exposed some critical vulnerabilities, particularly in cybersecurity. IoHT is characterized by interconnected medical devices sharing sensitive patient data, which amplifies the risk of cyber threats. Therefore, ensuring healthcare data’s integrity, confidentiality, and availability is essential. This study proposes a hybrid deep learning-based intrusion detection system that uses an Artificial Neural Network (ANN) with Bidirectional Long Short-Term Memory (BLSTM) and Gated Recurrent Unit (GRU) architectures to address critical cybersecurity threats in IoHT. The model was tailored to meet the complex security demands of IoHT and was rigorously tested using the Electronic Control Unit ECU-IoHT dataset. The results are impressive, with the system achieving 100% accuracy, precision, recall, and F1-Score in binary classifications and maintaining exceptional performance in multiclass scenarios. These findings demonstrate the potential of advanced AI methodologies in safeguarding IoHT environments, providing high-fidelity detection while minimizing false positives.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Malware Detection Techniques · Network Security and Intrusion Detection · IoT and Edge/Fog Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department Information Technology, College of Computers and Information Technology Taif University, Taif 21944, Saudi Arabia
Funding
Taif University, Saudi Arabia
References (29)
Show source-provided references
1. Internet of Things and Big Data as Enablers for Business Digitalization Strategies10.1016/j.technovation.2020.102173 ↗
2. Kamruzzaman, M.M. (2022). Key Technologies, Applications and Trends of Internet of Things for Energy-Efficient 6G Wireless Communication in Smart Cities. Energies, 15.10.3390/en15155608 ↗
3. Sunyaev, A. (2020). Internet Computing: Principles of Distributed Systems and Emerging Internet-Based Technologies, Springer International Publishing.10.1007/978-3-030-34957-8 ↗
4. Methods and Applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in Smart Energy Management10.1016/j.egyai.2022.100208 ↗
5. Olawale, O.P., and Ebadinezhad, S. (2023). Mobile Computing and Sustainable Informatics: Proceedings of ICMCSI 2023, Springer.
6. Which Algorithm Can Detect Unknown Attacks? Comparison of Supervised, Unsupervised and Meta-Learning Algorithms for Intrusion Detection10.1016/j.cose.2023.103107 ↗
7. Machine Learning towards Intelligent Systems: Applications, Challenges, and Opportunities10.1007/s10462-020-09948-w ↗
8. An Intelligent and Efficient Network Intrusion Detection System Using Deep Learning10.1016/j.compeleceng.2022.107764 ↗
9. Hybrid Intrusion Detection System for Internet of Things (IoT)10.36548/jismac.2020.4.002 ↗
10. Awajan, A. (2023). A Novel Deep Learning-Based Intrusion Detection System for IoT Networks. Computers, 12.10.3390/computers12020034 ↗
11. Chaganti, R., Suliman, W., Ravi, V., and Dua, A. (2023). Deep Learning Approach for SDN-Enabled Intrusion Detection System in IoT Networks. Information, 14.10.3390/info14010041 ↗
12. Musleh, D., Alotaibi, M., Alhaidari, F., Rahman, A., and Mohammad, R.M. (2023). Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT. J. Sens. Actuator Netw., 12.10.3390/jsan12020029 ↗
13. Anomaly-Based Intrusion Detection System for IoT Networks through Deep Learning Model10.1016/j.compeleceng.2022.107810 ↗
14. “Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks10.1109/OJCOMS.2022.3188750 ↗
15. Intrusion Detection System on IoT with 5G Network Using Deep Learning10.1155/2022/9304689 ↗
16. Kumar, A.K., Vadivukkarasi, K., and Dayana, R. (2023, January 9–11). A Novel Hybrid Deep Learning Model for Botnet Attacks Detection in a Secure IoMT Environment. Proceedings of the 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS), Coimbatore, India.10.1109/ICISCoIS56541.2023.10100396 ↗
17. Distributed Attack Detection Scheme Using Deep Learning Approach for Internet of Things10.1016/j.future.2017.08.043 ↗
18. Role of Internet of Things and Artificial Intelligence for Healthcare Informatics: An Overview
19. Evolving Technologies: IoT and Artificial Intelligence for Healthcare Informatics
20. Balas, V.E., Kumar, R., and Srivastava, R. (2020). Recent Trends and Advances in Artificial Intelligence and Internet of Things, Springer.10.1007/978-3-030-32644-9 ↗
21. IoT and Artificial Intelligence Implementations for Remote Healthcare Monitoring Systems: A Survey
22. A Comprehensive Review of ‘Internet of Healthcare Things’: Networking Aspects, Technologies, Services, Applications, Challenges, and Security Concerns10.1016/j.cosrev.2023.100591 ↗
23. Internet of Healthcare Things: A Contemporary Survey10.1016/j.jnca.2021.103179 ↗
24. Authentication and Identity Management of IoHT Devices: Achievements, Challenges, and Future Directions10.1016/j.cose.2021.102491 ↗
25. Alkhudhayr, F., Alfarraj, S., Aljameeli, B., and Elkhdiri, S. (2019, January 1–3). Information Security: A Review of Information Security Issues and Techniques. Proceedings of the 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia.10.1109/CAIS.2019.8769504 ↗
26. ECU-IoHT: A Dataset for Analyzing Cyberattacks in Internet of Health Things10.1016/j.adhoc.2021.102621 ↗
27. Vijayakumar, K.P., Pradeep, K., Balasundaram, A., and Prusty, M.R. (2023). Enhanced Cyber Attack Detection Process for Internet of Health Things (IoHT) Devices Using Deep Neural Network. Processes, 11.10.3390/pr11041072 ↗
28. Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R.M., Han, M., and Batista, D.M. (2023, January 17–20). Intrusion Detection System for IoHT Devices Using Federated Learning. Proceedings of the IEEE INFOCOM 2023-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Hoboken, NJ, USA.10.1109/INFOCOMWKSHPS57453.2023.10225932 ↗
29. Hussain, F., Abbas, S.G., Shah, G.A., Pires, I.M., Fayyaz, U.U., Shahzad, F., Garcia, N.M., and Zdravevski, E. (2021). A Framework for Malicious Traffic Detection in IoT Healthcare Environment. Sensors, 21.10.3390/s21093025 ↗