Medical Cyber-physical Systems Research Articles

Out-of-distribution Detection in Dependent Data for Cyber-physical Systems with Conformal Guarantees

Uncertainty in the predictions of learning-enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning-enabled component (LEC) is one source of uncertainty in the LEC’s predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher’s method leads to the proposed detector CODiT with bounded false alarms. CODiT performs OOD detection on fixed-length windows of consecutive time-series datapoints by using Fisher value of the input window. We further propose performing OOD detection on real-time time-series traces of variable lengths with bounded false alarms. This can be done by using CODiT to compute Fisher values of the sliding windows in the input trace and combining these values by a merging function. Merging functions such as Harmonic Mean, Arithmetic Mean, Geometric Mean, Bonferroni Method, and so on, can be used to combine Fisher values of the sliding windows in the input trace, and the combined value can be used for OOD detection on the trace with bounded false alarm rate guarantees. We illustrate the efficacy of CODiT by achieving state-of-the-art results in two case studies for OOD detection on fixed-length windows. The first one is on an autonomous driving system with perception (or vision) LEC. The second case study is on a medical CPS for walking pattern or GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject’s body. For OOD detection on variable length traces, we consider the same case studies on the autonomous driving system and medical CPS for GAIT analysis. We report our results with four merging functions on the Fisher values computed by CODiT on the sliding windows of the input trace. We also compare the false alarm rate guarantees by these four merging functions in the autonomous driving system case study. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD .

A Medical Cyber-Physical System utilizing the Bayes algorithm for post-diagnosis patient supervision

A Medical Cyber-Physical System utilizing the Bayes algorithm for post-diagnosis patient supervision

A Cyber Risk Assessment Approach to Federated Identity Management Framework-Based Digital Healthcare System.

This paper presents a comprehensive and evidence-based cyber-risk assessment approach specifically designed for Medical Cyber Physical Systems (MCPS)- and Internet-of-Medical Devices (IoMT)-based collaborative digital healthcare systems, which leverage Federated Identity Management (FIM) solutions to manage user identities within this complex environment. While these systems offer advantages like easy data collection and improved collaboration, they also introduce new security challenges due to the interconnected nature of devices and data, as well as vulnerabilities within the FIM and the lack of robust security in IoMT devices. To proactively safeguard the digital healthcare system from cyber attacks with potentially life-threatening consequences, a comprehensive and evidence-based cyber-risk assessment is crucial for mitigating these risks. To this end, this paper proposes a novel cyber-risk assessment approach that leverages a three-dimensional attack landscape analysis, encompassing existing IT infrastructure, medical devices, and Federated Identity Management protocols. By considering their interconnected vulnerabilities, the approach recommends tailored security controls to prioritize and mitigate critical risks, ultimately enhancing system resilience. The proposed approach combines established industry standards like Cyber Resilience Review (CRR) asset management and NIST SP 800-30 for a comprehensive assessment. We have validated our approach using threat modeling with attack trees and detailed attack sequence diagrams on a diverse range of IoMT and MCPS devices from various vendors. The resulting evidence-based cyber-risk assessments and corresponding security control recommendations will significantly support healthcare professionals and providers in improving both patient and medical device safety management within the FIM-enabled healthcare ecosystem.

CPU-GPU Cooperative QoS Optimization of Personalized Digital Healthcare Using Machine Learning and Swarm Intelligence.

In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.

A blockchain-based deep learning approach for cyber security in next-generation medical cyber-physical systems

<p>Cyber-physical systems (CPSs) have been employed to seamlessly integrate numerous processes and physical components with integrated computing facilities and data storage, aiming to achieve a heightened level of effectiveness and efficiency across various qualitative and quantitative metrics, including technical and organizational aspects. The increased use of the web and the prospering network through IoT (Internet of things) have given a critical open door to CPS to prevail. While this innovation is as of now utilized in programmed pilot flying, advanced mechanics frameworks, clinical checking, modern control frameworks, and so forth, the headway of these frameworks should understand undeniable spotlight on making them proficient and secure. To work on the strength, reliability, and security of these frameworks, specialists can integrate blockchain innovation which has an inbuilt mix of consensual calculations, secure conventions, and circulated information capacity, with the CPS. This introduces an efficient deep learning approach based on blockchain for medical cyber-physical systems (CPS), consisting primarily of two components: a) a blockchain based security framework to protect the medical data and b) the extraction of quintessential features from these data to a classifier for performing the anomaly scans using deep learning. The experimental evaluation demonstrates that the suggested system outperforms existing models, achieving exceptional performance with an accuracy rate of 0.96 and a sensitivity score of 0.95.</p>

Federated Learning-based Information Leakage Risk Detection for Secure Medical Internet of Things

The Medical Internet of Things (MIoT) requires extreme information and communication security, particularly for remote consultation systems. MIoT’s integration of physical and computational components creates a seamless network of medical devices providing high-quality care via continuous monitoring and treatment. However, traditional security methods such as cryptography cannot prevent privacy compromise and information leakage caused by security breaches. To solve this issue, this paper proposes a novel Federated Learning Intrusion Detection System (FLIDS). FLIDS combines Generative Adversarial Network (GAN) and Federated Learning (FL) to detect cyber attacks like Denial of Service (DoS), data modification, and data injection using machine learning. FLIDS shows exceptional performance with over 99% detection accuracy and 1% False Positive Rate (FPR). It saves bandwidth by transmitting 3.8 times fewer bytes compared to central data collection. These results prove FLIDS’ effectiveness in detecting and mitigating security threats in Medical Cyber-Physical Systems (MCPS). The paper recommends scaling up FLIDS to use computing resources from multiple mobile devices for better intrusion detection accuracy and efficiency while reducing the burden on individual devices in MIoT.

Securing Pacemakers using Runtime Monitors over Physiological Signals

Wearable and implantable medical devices (IMDs) are increasingly deployed to diagnose, monitor, and provide therapy for critical medical conditions. Such medical devices are safety-critical cyber-physical systems (CPSs). These systems support wireless features introducing potential security vulnerabilities. Although these devices undergo rigorous safety certification processes, runtime security attacks are inevitable. Based on published literature, IMDs such as pacemakers and insulin infusion systems can be remotely controlled to inject deadly electric shocks and excess insulin, posing a threat to a patient’s life. While prior works based on formal methods have been proposed to detect potential attack vectors using different forms of static analysis, these have limitations in preventing attacks at runtime. This paper discusses a formal framework for detecting cyber-physical attacks on a pacemaker by monitoring its security policies at runtime. We propose a wearable device that senses the Electrocardiogram (ECG) and Photoplethysmogram (PPG) of the body to detect attacks in a pacemaker. To facilitate the design of this device, we map the security policies of a pacemaker w.r.t ECG and PPG, paving the way for designing formal verification monitors for pacemakers for the first time using multiple physiological signals. The proposed monitoring framework allows the synthesis of parallel monitors from a given set of desired security policies, where all the monitors execute concurrently and generate an alarm to the user in the case of policy violation. Our implementation and the performance evaluation results demonstrate the technical feasibility of designing such a wearable device for attack detection in pacemakers. This device is separate from the pacemaker, ensuring no need for re-certification of pacemakers. Our approach is amenable to the application of security patches when new attack vectors are detected, making the approach ideal for runtime monitoring of medical CPSs.

Explainable AI for Medical Image Analysis in Medical Cyber-Physical Systems: Enhancing Transparency and Trustworthiness of IoMT.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

A Digital Twin-Based Platform for Medical Cyber-Physical Systems

A Digital Twin-Based Platform for Medical Cyber-Physical Systems

Blockchain-Enabled Hyperledger Fabric to Secure Data Transfer Mechanism for Medical Cyber-Physical System: Overview, Issues, and Challenges

This paper proposes a model to address the challenges faced by medical cyber-physical systems (MCPS) by implementing a permissioned blockchain platform. The platform incorporates the unique properties of blockchain into the network of affected systems, including decentralization, transparency, and immutability. The platform also includes a novel technique to secure MCPS through an automated access-control manager. This manager allows users to control who has access to their data, and can be configured to trust a third party if desired. The paper also extends into networked medical device systems, and discusses how the platform can be used to address critical is-sues specific to this field, such as network design. Finally, the paper discusses how various security features can be integrated into ultra-small devices, enhancing the protection of embedded systems. The overall objective of this research is to develop a secure and efficient data transfer mechanism for MCPS. The proposed platform addresses the challenges faced by MCPS by incorporating the unique properties of blockchain.

Provably secure and lightweight three-factor authentication scheme for industrial medical CPS

Cyber-Physical System (CPS) is a multidimensional complex system that integrates computing, network, and physical environment, which is widely used to promote the upgrading of industrial production and technology. Recently, the application of industrial CPS in the medical field has attracted the attention of scholars and medical experts. Medical CPS can establish a perfect medical network to help doctors monitor patients’ conditions in real-time and make treatments. However, how to design a provably secure and lightweight authentication protocol for industrial medical CPS is a challenge. Very recently, Qi et al. proposed an authentication protocol for industrial medical CPS based on the chaotic map, the Artificial Intelligence (AI) biometric technique is used in the protocol to resist password guessing attack and smart card lost attack. However, we find that their protocol is still vulnerable to identity guessing attack, user impersonation attack, trace attack, desynchronization attack, and has no perfect forward secrecy. Therefore, we propose a security-enhanced and lightweight authentication protocol for industrial medical CPS. In the protocol, a dynamic temporary identity strategy is designed to protect anonymity and privacy, which enables the updating of temporary identities while resisting desynchronization attacks. The protocol is proved secure through formal security proof in random oracle model. Meanwhile, compared with the related protocols, our protocol is superior in security and cost to meet the lightweight requirements in medical scenarios.

A Medical Cyber-physical system for predicting maternal health in developing countries using machine learning

It is essential to monitor any health issues during pregnancy to ensure a safe delivery because pregnancy is crucial for both mother and child. However, developing countries have poor access to healthcare, making managing possible health risks during pregnancy challenging. An Internet of Things (IoT)-based Medical Cyber-Physical System (MCPS) can offer a valuable and affordable solution for anticipating and controlling health hazards during pregnancy to solve this issue. This paper presents the design and development of an MCPS for recognizing health risks in pregnant women in developing countries. The system collects key health metrics using temperature, blood pressure, glucose levels, and heart rate sensors. It automatically considers risk factors to predict health risks using Machine Learning (ML) and sends them to the nearest clinic or hospital. Patients can manually enter their risk factors into the program and talk with a doctor through it. The efficacy of the proposed MCPS is evaluated using a dataset of pregnant women, and the results demonstrate that the system can accurately detect health issues during pregnancy. Medical experts can.enhance maternal and fetal health outcomes using the systems real-time data collecting and processing capabilities. Despite restricted access to healthcare in developing countries, the proposed MCPS provides a valuable and economical method of addressing pregnancy-related health risks. The MCPS can assist medical personnel in making quick and informed choices, enhancing the level of care provided to expectant mothers and their unborn children.

Cogni-Sec: A secure cognitive enabled distributed reinforcement learning model for medical cyber–physical system

The advent of the Internet of Things (IoT) has resulted in significant technical development in the healthcare sector, enabling the establishment of Medical Cyber–Physical Systems (MCPS). The increased number of MCPS generates a massive amount of privacy-sensitive data, hence it is important to enhance the security of devices and data transmission in MCPS. Earlier several research studies were undertaken in order to enhance security in healthcare, but none of them could adapt to changing behaviors of data attacks. Here the role of blockchain and Reinforcement Learning (RL) comes into play since it can adjust itself to the nature of changing attacks, thus preventing any kind of attacks. This work proposes a solution, named Cogni-Sec, which employs a decentralized cognitive blockchain and Reinforcement Learning architecture and addresses the security issue. Blockchain is incorporated in the approach for data storage and transmission to increase the degree of security in the MCPS modules. Hyperledger Fabric is applied as the blockchain base which shows transaction query results with nearly 10% increased throughput, 69% less memory consumption, and 15% lower CPU usage when compared to Ethereum. Further security risk at the block mining level within a blockchain network is reduced by introducing distributed Reinforcement Learning architecture in replacement for the miner nodes, which imitates the cognitive behavior of miners in a distributed environment. Different multi-agent learning systems have been evaluated for building the mining agent. Among these, the a3c agent in distributed learning setup yields the optimum cumulative reward with a median value of 54.5 and minimizes the maximum number of data threats.

A solution to smart health and state of art

A medical cyber–physical system (MCPS) is a unique cyber–physical system (CPS), which combines embedded software control devices, networking capabilities, and complex physiological dynamics of patients in the modern medical field. In the process of communication, device, and information system interaction of MCPS, medical cyber–physical data are generated digitally, stored electronically, and accessed remotely by medical staff or patients. With the advent of the era of medical big data, a large amount of medical cyber–physical data is collected, and its sharing provides great value for diagnosis, pathological analysis, epidemic tracking, pharmaceutical, insurance, and so on. This overview will present MCPS’s architectures and frameworks from different perspectives, modeling and verification methods, identification and sign sensing technologies, key communications’ technologies, data storage and analysis technologies, monitoring systems, data security and privacy protection technologies, and key research perspectives and directions. We can have a com- prehensive understanding of the important characteristics and technical route of MCPS, and grasp its research status and progress.

An intelligent Medical Cyber–Physical System to support heart valve disease screening and diagnosis

Cardiovascular diseases are currently the major causes of death globally. Among the strategies to prevent cardiovascular issues, the automated classification of heart sound abnormalities is an efficient way to detect early signs of cardiac conditions leading to heart failure or other, even asymptomatic, complications, quite effective for timely interventions. Despite the significant improvements in this field, there are still limitations due to the lack of solutions, available data-sets and poor (mainly binary — normal vs abnormal) classification models and algorithms. This paper presents a Medical Cyber–Physical System (MCPS) for the automatic classification of heart valve diseases onsite, in a timely manner. The proposed MCPS, indeed, can be deployed into personal and mobile devices, addressing the limitations of existing solutions for patients, healthcare practitioners, and researchers, through an efficient and easy accessible tool. It combines different neural network models trained on a new Italian dataset of 132 adult patients covering 9 heart sound categories (1 normal and 8 abnormal), also validated against two main open-access (Physionet/CinC Challenge 2016 and Korean) datasets. The overall MCPS performance (time, processing and energy resource utilization) and the high accuracy of the models (up to 98%) demonstrated the feasibility of the proposed solution, even with few data. The dataset supporting the findings of this paper is available upon request to the authors.

Ensuring authentication in Medical Cyber-Physical Systems: A comprehensive literature review of blockchain technology integration with machine learning

Ensuring authentication in Medical Cyber-Physical Systems: A comprehensive literature review of blockchain technology integration with machine learning

A fully automated normothermic machine perfusion system for kidney grafts supporting physiological motivated flow profiles

Abstract Research showed that the normothermic machine perfusion of kidneys can enable prolonged storage and improve conditions compared to traditional cold storage. For research in this area, there is a demand for a long-term in vitro perfusion setup. In this work, we present a fully automated normothermic machine perfusion (NMP) system as an experimental research platform. The perfusion system is intended as a tool for researching the effects of different perfusion strategies on the kidney. To enable the automation, the NMP system consists of a blood pressure control, a circulation volume level control and a pH-regulation component. The setup is realized as a medical cyber-physical system consisting of networked embedded microcontroller nodes.

FCMCPS-COVID: AI propelled fog–cloud inspired scalable medical cyber-physical system, specific to coronavirus disease

Medical cyber–physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.

Lightweight certificateless privacy-preserving integrity verification with conditional anonymity for cloud-assisted medical cyber–physical systems

Cloud-assisted medical cyber–physical systems (MCPSs) leverage the power of cloud computing for scalable storage and management of outsourced medical data. Based on the crucial outsourced medical data, guardians could know patient’s health condition timely, doctors can make accurate diagnoses of patients, and medical research centers could also conduct medical big data analysis. However, medical data is highly sensitive and demands strong protection. Meanwhile, the integrity of outsourced medical data is vulnerable to hardware failures, software bugs or human errors. To date, a large number of data privacy-preserving integrity auditing schemes have been presented, but most of them suffer from the burden of complex public key certificate management or the key escrow problem of identity-based cryptography. In this paper, we propose a lightweight certificateless privacy-preserving integrity verification scheme (LCPPIV) with conditional anonymity for cloud-assisted MCPSs by developing an elliptic-curve digital signature and a key exchange mechanism, which can achieve the functionalities of medical data privacy protection, integrity verification of outsourced medical data, conditional anonymity, batch auditing, and secure compensation mechanism, simultaneously. Theoretical security analysis demonstrates that our scheme is provably secure in the random oracle model, with resistance to the public key replacement attack, malicious-but-passive-KGC attack, and response auditing proofs forgery. The performance evaluations indicate that LCPPIV is much more practical for cloud-assisted MCPSs compared with state-of-art data auditing schemes.

Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm

A medical cyber-physical system (MCPS) integrates medical sensor devices with cyber (information) components, which creates a sensitive approach and provides security. The MCPS plays a vital role in hospitals by detecting attacks and protecting patients' medical information. Many research projects have been carried out to detect attacks on the generation of medical information in MCPS. The issues with existing algorithms are their inefficiency and time-consuming maximization of error rates. To overcome the challenges, this paper proposes detecting the attack using the fuzzy C-Means algorithm with artificial bee colony optimization (FCM-ABC). The novelty of the work is flexibility in deciding whether data points belong to actual users or attackers using the fuzzy c means degree [0,1] measuring technique of cluster formation. ABC is used in self-organizing the clusters with ABC collective intelligence. It monitors health information using sensor networks. The MCPS model interconnects the sensor devices remotely and collects the information. SVM has an accuracy rate of 76.32%, FCM has an accuracy rate of 81.34%, LSTM has an accuracy rate of 86.22%, and our proposed work, FCM-ABC, has an accuracy rate of 93.34%.