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Smart city article details
| Title | Securing Electronic Health Records From Insider Threats In Smart City Healthcare Cloud Using Machine Learning Approach |
|---|---|
| ID_Doc | 47740 |
| Authors | Ramasami S.; Uma Maheswari P. |
| Year | 2024 |
| Published | Proceedings - 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024 |
| DOI | http://dx.doi.org/10.1109/ICICV62344.2024.00107 |
| Abstract | In recent years, healthcare in smart cities is considered as significant to create a more resilient and well-informed healthcare ecosystem. The integration of cloud computing in healthcare industry has facilitated the storage and sharing of electronic health records (EHRs), enabling seamless access to medical information. However, this transition to cloud-based systems has introduced new security challenges, particularly the risk of insider threats. Insiders with authorized access to cloud-based EHR systems, such as healthcare professionals and cloud service providers, may exploit vulnerabilities or misuse sensitive patient data, leading to privacy breaches and unauthorized disclosure. This research work presents an innovative approach to securing EHRs from insider threats in the cloud using Gaussian Mixture Model and Classification algorithms. These techniques analyze user behaviour patterns and detect anomalous activities that may indicate insider threats. Here UK hospital electronic patient health record data set is used to conduct the experiments. The proposed approach comprises two key stages. In the initial stage, the unsupervised Gaussian Mixture Model is utilized to find the abnormal patterns in the dataset and label each record in the dataset as normal or anomaly. The second stage involves different supervised classification algorithms namely SVM, KNN, DT, NB and RF are used to classify the new instance. The results demonstrate that the Random Forest (RF) achieves high accuracy in detecting insider threats with an accuracy of 99.97%. The findings of this research contribute to the field of healthcare data security by offering an intelligent and proactive solution for mitigating insider threats to cloud-based EHRs in Smart city environments. © 2024 IEEE. |
| Author Keywords | Anomaly detection; Classification; Clustering; Electronic health record; Machine learning; Smart city healthcare cloud |
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