Security and Cybersecurity Risk Management in E-Health Systems: A Hybrid Approach

Though it has concurrently produced unprecedented levels of security concerns, the fast digitalization of healthcare services via the use of e-health systems has revolutionized the delivery of patient care. This is despite the fact that it has simultaneously transformed the delivery of patient care. In this article, an investigation is conducted into the present state of study concerning the management of cybersecurity risks and security in EHR systems. This study illustrates the unique weaknesses, existing approaches, and creative solutions that are now being applied in this vitally important issue. As a consequence, the study exposes the individual flaws. Because of the sensitive nature of the information that they handle and the critical nature of the data that they manage, EHRs provide a unique set of security concerns. The difficulties that are brought about by the combination of historical systems and contemporary security procedures may be classified into three distinct categories: technological, organizational, and human. In addition to these challenges, there is also the challenge of integrating old security processes with contemporary ones.

Their in-depth review of 49 studies found that organizational and human variables continue to be key inadequacies in healthcare settings, despite the fact that technical solutions are continually improving. This was the conclusion reached by the researchers. In comprehensive research that Fernández-Alemán et al. [5] carried out, it was demonstrated that healthcare professionals commonly prioritize data access over security, which results in a fundamental conflict in healthcare information systems. This conflict is a result of the fact that data access is prioritized more than security. On the basis of the findings of the investigation, this viewpoint is justified. Their examination of thirty-one articles revealed that the healthcare industry is not as developed as other sectors in terms of cybersecurity. This was the conclusion reached by the researchers. This is because there are limited resources and conflicting interests, both of which often push security to a secondary obligation. As a result, this situation has arisen.

When it comes to the security measures that are put into place in healthcare settings, the regulatory environment is the one that has a considerable influence. Specifically, legislation such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, the General Data Protection Regulation (GDPR) in Europe, and other frameworks all over the world demand special security measures and patient privacy safeguards, as stated by Tschider [6], who conducted research on this subject. Pussewalage and Oleshchuk [7] demonstrated through their study on privacy-protecting devices that adherence to these rules does not necessarily ensure perfect safety. This is despite the fact that the guidelines have been established. As a result of their investigation, they came to the conclusion that there are major discrepancies between legislative compliance and real security efficacy. This is especially true in distributed e-health systems, where traditional security perimeters are becoming increasingly unclear.

In the healthcare business, the conventional approach to security risk management has depended mostly on well-established approaches such as NIST SP 800-30, ISO 27005, and OCTAVE. This has been the case continuously since the beginning, as demonstrated by Caralli et al. [8], who conducted an investigation of the effectiveness of these approaches in healthcare settings. They discovered that these methods frequently give point-in-time evaluations, which quickly become obsolete in circumstances where the threat landscape is constantly evolving. Through their examination of security concerns that were present in a variety of e-health systems, they were able to shed light on both the benefits and drawbacks of static risk assessment methodologies. Sardi et al. [9], who carried out an exhaustive literature analysis on the subject of cyber risk in healthcare facilities, adopted a strategy that was quite similar to those described above. In hospital settings, where operational requirements regularly come into conflict with security best practices, they uncovered 41 relevant publications that together emphasized the obstacles involved with applying traditional security frameworks. Together, these studies underlined the issues that came with implementing these frameworks.

The panorama of monitoring undertaken by healthcare institutions has become more challenging as a consequence of the COVID-19 epidemic. Within the context of the pandemic, Mohapatra et al. [10] carried out an examination of the issues regarding security and privacy that were brought about by the fast development of digital technology. Due to the fact that our analysis concentrated especially on telehealth services and remote monitoring systems, the attack surface for healthcare professionals was dramatically increased by a large amount. They demonstrated, by means of their painstaking investigation of contact tracing applications, how the emergency installation of health technology can frequently fail to perform the necessary security risk evaluation. This, in turn, can result in vulnerabilities that continue to exist even after the initial crisis response has been completed.

In recent years, a number of studies have been conducted to study the potential to improve cybersecurity in the healthcare industry through the application of ML techniques. Vinayakumar et al. [11] showed in their research that deep learning models are more successful than traditional methods in identifying malicious software that is aimed at healthcare systems. Traditional signature-based methods had substantially lower detection rates compared to our models, which had significantly greater detection rates. They conducted detailed research, which made use of data acquired from actual hospital networks. The results of this analysis suggested that there were unique possibilities in finding risks that had not been reported previously. Choudhury and Asan [12] stated that supervised learning techniques were applied in order to identify probable data breaches in EHR systems. This was done in order to prevent data breaches from occurring. These security vulnerabilities were caused by access patterns that were not typical. Their method was able to identify suspicious conduct with an accuracy rate of 89% when tested on anonymised hospital access data. This means that it is superior than rule-based systems in terms of its ability to identify suspicious behavior. Furthermore, it resulted in a 35% reduction in the average number of false positives.

A significant amount of success has been achieved in the realm of healthcare security through the utilization of anomaly detection. Chen et al. [13] were able to uncover unexpected patterns in the access to EHRs by employing unsupervised learning. These patterns may imply that improper utilization of the information was taking place. They produced ground-breaking research that demonstrated that it is feasible to discover atypical insiders by exploiting the views of collaborative information systems. This research was a significant step forward in the field. In order to lessen the number of false positives that were generated by intrusion detection systems specifically designed for healthcare networks, Alsolami et al. [14] utilized ensemble learning strategies that included a wide variety of detection strategies. The aforementioned statement served as the foundation for our action. Their decision tree-based technique outperformed single-algorithm solutions by a margin of 27% when it was evaluated against typical threat vectors for healthcare systems. This was the case when it came to the accuracy of detection.

Researchers in the academic sector have been motivated to examine hybrid systems that incorporate the advantages of both conventional and pure ML-based techniques due to the limits of both of these approaches. According to Baz et al. [15], developing healthcare security solutions that are more responsive might be done by combining behavioral analytics with traditional risk assessment. This was stated in the article. The smart fusion model that they developed displayed exceptional performance in authentication circumstances that are typically encountered during clinical procedures. Islam et al. [16] employed neural networks in conjunction with formal risk analysis in order to increase the accuracy of risk prediction in healthcare information systems. This was done in a manner that is comparable to the previous example. They were able to effectively solve the specific security challenges that are connected with integrated healthcare delivery networks by utilizing their enterprise cybersecurity risk quantification approach. This allowed them to properly address the issues. A great number of studies have been conducted to study the ways in which specific ML approaches could be able to aid with concerns regarding healthcare security. Additionally, in the year 2020, Masud et al. [17] designed a unified architecture with the intention of assuring the security of information stored in cloud-based e-health systems as well as the storage of such information. The standard security methods and the adaptive learning components are both incorporated into this design. The solution that they built addressed the unique issues that occur when seeking to protect patient information in cloud settings, which are characterized by a lack of clear boundaries between traditional security and cloud environments. What the authors of the narrative study that Coventry and Branley [18] did on the topic of cybersecurity trends in the healthcare sector revealed was that there is a need for security policies that are more adaptive and capable of adapting to the fast-altering threat environment. This was discovered by the writers of the study. As a result of their research, they were able to demonstrate that ML had the ability to successfully handle the constantly shifting nature of cybersecurity threats in healthcare settings.

The studies also reveal that there are considerable problems connected with the implementation of effective security risk management in healthcare settings. According to the findings of Li et al. [1], who conducted an investigation into the utilization of e-health systems by healthcare professionals, the most important obstacle to adoption was concerns over security. The inconsistency that exists between the efficiency of clinical procedure and the requirement for security was brought to light by the careful investigation that they carried out on 93 relevant studies. According to the annual "Cost of a Data Breach Report" published by the Ponemon Institute, the healthcare business is routinely ranked as having the highest per-record cost of data breaches among all industries. The average cost of a data breach was determined to be $9.23 million in the analysis for the year 2023, which highlights the crucial need of good security risk management in this industry.

There is a possibility that human factors constitute the most major weakness in the healthcare security system. A situation in which an excessive number of security demands leads to healthcare professionals inventing workarounds that ultimately harm the security of the system is referred to as "security fatigue," and the term "security fatigue" was adopted by Furnell and Thomson [19] to characterize the situation. In the course of their investigation of user behavior in high-security environments, the researchers came to the realization that physicians typically place a higher priority on the speed with which they provide patient care than they do on the rules that govern security. As a consequence, this leads to the establishment of large vulnerabilities that technology protections are unable to remedy on their own.

In the recently suggested research path, the utilization of more integrated solutions that complement technological limitations with human concerns and administrative requirements is advocated. An investigation that was carried out not too long ago suggested the adoption of a risk assessment system that is aware of the context and dynamically adapts security measures in line with the particular clinical circumstance. This system has the capacity to combine the operational requirements of healthcare with the necessity for security in a way that is both effective and efficient. Jalali and Kaiser [20] created a hybrid security architecture that combines traditional perimeter defenses with behavioral monitoring and anomaly detection based on ML. This architecture was built in the meantime. When it came to recognizing complex assaults that were aimed at healthcare infrastructure, this architecture displayed exceptional performance.

In the current research landscape, it has been discovered that there are significant gaps, which are demonstrated by this analysis of the literature. Although a large number of studies have studied either traditional risk assessment techniques or ML technologies in isolation, only a relatively small number of these studies have built complete frameworks that successfully include both approaches. To begin, this is despite the fact that a large number of studies have investigated either of these approaches. In light of this, the bulk of the studies that are presently being carried out concentrate on the detection of threats rather than complete risk management, which encompasses activities such as the identification, evaluation, mitigation, and monitoring of risk. Third, there is a lack of empirical validation for the suggested security frameworks in actual healthcare settings; the majority of research focuses on simulated data or small pilot projects. This is a problem because the frameworks are supposed to be used in patients.

A hybrid approach to security and cybersecurity risk management in e-health systems is proposed in the work that is addressed in this article. This method helps to fill in the gaps that have been identified. This strategy combines conventional approaches to risk assessment with technologies that are founded on the concept of ML. In an environment that is becoming increasingly digital and networked, the objective of our approach is to deliver a solution that is more effective in addressing the unique security concerns that healthcare companies are confronted with. It is possible to do this by combining the flexible capabilities of ML with the extensive coverage that is provided by older methods.

To validate our hybrid approach, we implemented a proof-of-concept system and evaluated it using real-world healthcare datasets. This section details the implementation architecture, data sources, preprocessing techniques, and experimental configuration.

A. System Architecture

Our implementation follows a modular architecture comprising four primary components:

1. Data Collection Module: Interfaces with e-health systems to gather logs, network traffic, access patterns, and system configuration information
2. Risk Analysis Engine: Implements the traditional risk assessment methodology described in Section III-B
3. ML Module: Contains the supervised classification and unsupervised anomaly detection components
4. Security Control Recommendation System: Maps identified risks to appropriate security controls

The system was implemented using Python 3.9 with the following key libraries:

• Scikit-learn for ML algorithms
• Pandas and NumPy for data manipulation
• Flask for the web-based dashboard interface
• PyTorch for deep learning components

B. Datasets

We utilized three complementary datasets for our experiments:

1. Healthcare Breach Dataset: A compilation of 1,500 documented healthcare data breaches from 2018-2023, including breach details, affected systems, and root causes
2. Synthetic E-Health Logs: Generated log data simulating typical e-health system operations with injected security incidents
3. Real-world Anonymized Access Logs: De-identified access logs from a medium-sized healthcare provider's EHR system, covering six months of operations

The datasets were split into training (70%), validation (15%), and testing (15%) sets, with careful attention to maintaining the chronological nature of the data to prevent data leakage.

C. Feature Engineering

We extracted 37 features from the raw data, categorized as:

1. Temporal Features: Time patterns of system access and operations
2. Behavioural Features: User access patterns and deviations from role-based norms
3. Network Features: Communication patterns between system components
4. Content Features: Characteristics of data being accessed or transmitted

Feature selection was performed using recursive feature elimination with cross-validation (RFECV), resulting in 23 optimal features for the final models.

D. Model Training and Evaluation

The supervised classification model was trained using gradient boosting with the following hyperparameters optimized through grid search:

• Learning Rate: 0.05
• Maximum Depth: 6
• Number of Estimators: 200
• Subsample: 0.8

For anomaly detection, we compared isolation forests, one-class SVM, and an autoencoder approach, ultimately selecting isolation forests based on superior performance metrics.

Models were evaluated using standard security metrics, including:

• Precision, recall, and F1-score for the classification tasks
• Area under the ROC curve (AUC-ROC) for overall detection capability
• False positive rate (FPR) and detection rate (DR) for anomaly detection

E. Experimental Scenarios

We evaluated our approach under three experimental scenarios:

1. Baseline Scenario: Using only traditional risk assessment methods
2. ML-Only Scenario: Relying solely on ML for threat detection
3. Hybrid Scenario: Our proposed integrated approach

Each scenario was tested against common attack vectors relevant to healthcare environments, including:

• Phishing attacks targeting healthcare staff
• Unauthorized access to patient records
• Malware specifically targeting medical devices
• Insider threats involving data exfiltration

Although our hybrid strategy shows notable gains over conventional approaches, there are still some restrictions and possibilities for further research:

1. Data Limitations: Our evaluation relied partially on synthetic data due to the sensitivity of real healthcare security incidents. Broader access to actual incident data would enhance model training and validation.
2. Computational Overhead: The ML components introduce computational requirements that may be challenging for smaller healthcare organizations. Future work should focus on optimizing the models for deployment in resource-constrained environments.
3. Regulatory Integration: Currently, our framework addresses general security best practices but does not explicitly map to specific regulatory requirements. Extending the framework to automatically generate compliance documentation would enhance its practical utility.
4. Transferability: The models were trained on specific healthcare environments and may require recalibration for different organizational contexts. Developing transfer learning techniques to adapt the models to new environments with minimal retraining would be valuable.

Future research directions include:

1. Exploring federated learning approaches to enable collaborative threat intelligence sharing across healthcare organizations without compromising data privacy.

2. Integrating natural language processing to analyze clinical documentation for potential security implications.

3. Developing explainable AI techniques to increase trust and adoption among healthcare security professionals.

4. Extending the framework to address the unique security challenges of emerging technologies such as remote patient monitoring and AI-driven diagnostic systems.