Health Care Cyber-physical System Research Articles

PUFchain 3.0: Hardware-Assisted Distributed Ledger for Robust Authentication in Healthcare Cyber-Physical Systems.

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber-physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient's Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle's Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient's gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient's BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time.

Abscissa-Ordinate Focused Network for Psoriasis and Eczema Healthcare Cyber-Physical System With Active Label Smoothing

Abscissa-Ordinate Focused Network for Psoriasis and Eczema Healthcare Cyber-Physical System With Active Label Smoothing

Securing Smart Healthcare Cyber-Physical Systems against Blackhole and Greyhole Attacks Using a Blockchain-Enabled Gini Index Framework

The increasing reliance on cyber-physical systems (CPSs) in critical domains such as healthcare, smart grids, and intelligent transportation systems necessitates robust security measures to protect against cyber threats. Among these threats, blackhole and greyhole attacks pose significant risks to the availability and integrity of CPSs. The current detection and mitigation approaches often struggle to accurately differentiate between legitimate and malicious behavior, leading to ineffective protection. This paper introduces Gini-index and blockchain-based Blackhole/Greyhole RPL (GBG-RPL), a novel technique designed for efficient detection and mitigation of blackhole and greyhole attacks in smart health monitoring CPSs. GBG-RPL leverages the analytical prowess of the Gini index and the security advantages of blockchain technology to protect these systems against sophisticated threats. This research not only focuses on identifying anomalous activities but also proposes a resilient framework that ensures the integrity and reliability of the monitored data. GBG-RPL achieves notable improvements as compared to another state-of-the-art technique referred to as BCPS-RPL, including a 7.18% reduction in packet loss ratio, an 11.97% enhancement in residual energy utilization, and a 19.27% decrease in energy consumption. Its security features are also very effective, boasting a 10.65% improvement in attack-detection rate and an 18.88% faster average attack-detection time. GBG-RPL optimizes network management by exhibiting a 21.65% reduction in message overhead and a 28.34% decrease in end-to-end delay, thus showing its potential for enhanced reliability, efficiency, and security.

Recurrent Semantic Learning-Driven Fast Binary Vulnerability Detection in Healthcare Cyber Physical Systems

Healthcare cyber physical systems (HCPS) always pursuing high availability allow software providers to adopt multiple kinds of development languages to reuse third-party program codes, while leading to the wide propagation of hidden software vulnerabilities. However, it is impossible to accurately trace execution paths and locate the key elements during the software execution process, which makes semantic features of vulnerabilities in the binary code can not bed extracted. This is the key support in automated vulnerability detection practices. To address these problems, a novel fast vulnerability detection mechanism based on recurrent semantic learning is proposed, which does not require high-level permissions to access the compiling process and traverse all execution paths. Firstly, a programframe is constructed to integrate software run-time logic and executing environment, detecting vulnerabilities from multi-programming language binary codes. Secondly, to achieve the powerful software execution context-awareness ability, a cascaded-LSTM recurrent neural network is designated to extract semantic features from binary files with vulnerabilities. Besides, we establish an experimental toolkit named an intelligent vulnerability detector (IntVD) to demonstrate the effectiveness of the proposed methods. Extensive and practical experiments validate that the vulnerability recognition accuracy on the HCPS software including VLC and LibTIFF can reach more than 95%.

Data Analytic for Healthcare Cyber Physical System

Nowadays, a large number of AI-powered healthcare cyber-physical systems (CPSs) have been used in healthcare services. In order to provide better care, AI-powered healthcare CPSs analyze the data they collect using a variety of techniques. Data analysis for artificial intelligence (AI)-driven healthcare CPS is one of these approaches. However, none of the techniques in data analysis can provide a good representation of contiguous and negative information. Therefore, we are the first to introduce the problem of contiguous negative sequential pattern mining. A novel algorithm called Contiguous Negative Sequential Pattern Miner (CNSPM) is proposed to discover and analyze contiguous negative sequential patterns (CNSPs) from the data collected by healthcare CPSs. Finally, we select some real medical and non-medical datasets to conduct numerous experiments. We further analyze the discovered patterns and show how healthcare services can use meaningful patterns for medical decision-making. The performance results on these datasets demonstrate that the proposed algorithm can discover more valuable patterns efficiently and effectively from the collected and transformed medical data.

Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

Mobility and Privacy-Aware Offloading of AR Applications for Healthcare Cyber-Physical Systems in Edge Computing

Cyber-physical systems (CPSs) can be regarded as a new generation of systems which have been widely used for healthcare system. The introduction of Augmented Reality (AR) can further enhance the effectiveness of healthcare CPSs. AR applications can provide a better user experience in the health treatment process for both patients and clinicians. However, AR applications are computation-intensive, putting a substantial computational burden on AR devices. Fortunately, offloading AR applications to edge nodes can enable AR to be suitable for real-time applications. Nevertheless, AR applications deal with the patient’s private information; placing it on edge raises serious privacy concerns. Besides, the network structure of AR applications has spatio-temporal uncertainty. To tackle these issues, we jointly investigate the computation offloading for AR applications in the healthcare CPSs in edge computing considering user privacy protection and mobility. We propose a novel multi-objective meta-heuristic method based on the R2 indicator-II, which preserves privacy, and minimizes the Motion-to-photon latency, energy consumption, and maintain load balancing. Eventually, it verifies the efficiency and superiority of our proposed approach based on a certain scale of the experiments.

Attention-Based Multidimensional Deep Learning Approach for Cross-Architecture IoMT Malware Detection and Classification in Healthcare Cyber-Physical Systems

A literature survey shows that the number of malware attacks is gradually growing over the years due to the growing trend of Internet of Medical Things (IoMT) devices. To detect and classify malware attacks, automated malware detection and classification is an essential subsystem in healthcare cyber-physical systems. This work proposes an attention-based multidimensional deep learning (DL) approach for a cross-architecture IoMT malware detection and classification system based on byte sequences extracted from Executable and Linkable Format (ELF; formerly named Extensible Linking Format) files. The DL approach automates the feature design and extraction process from unstructured byte sequences. In addition, the proposed approach facilitates the detection of the central processing unit (CPU) architecture of the ELF file. A detailed experimental analysis and its evaluation are shown on the IoMT cross-architecture benchmark dataset. In all the experiments, the proposed method showed better performance compared with those obtained from several existing methods with an accuracy of 95% for IoMT malware detection, 94% for IoMT malware classification, and 95% for CPU architectures classification. The proposed method also suggests a similar performance with an accuracy of 94% on the Microsoft malware dataset. Experimental results on two malware datasets indicate that the proposed method is robust and generalizable in cross-architecture IoMT malware detection, classification, and CPU architectures classification in healthcare cyber-physical systems.

Post-Processing Fairness Evaluation of Federated Models: An Unsupervised Approach in Healthcare.

Modern Healthcare cyberphysical systems have begun to rely more and more on distributed AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning (ML) and Deep Learning (DL) models for the wide variety of medical fields, while at the same time fortifying the privacy of the sensitive information that are present in the medical sector, makes the FL technology a necessary tool in modern health and medical systems. Unfortunately, due to the polymorphy of distributed data and the shortcomings of distributed learning, the local training of Federated models sometimes proves inadequate and thus negatively imposes the federated learning optimization process and in extend in the subsequent performance of the rest Federated models. Badly trained models can cause dire implications in the healthcare field due to their critical nature. This work strives to solve this problem by applying a post-processing pipeline to models used by FL. In particular, the proposed work ranks the model by finding how fair they are by discovering and inspecting micro-Manifolds that cluster each neural model's latent knowledge. The produced work applies a completely unsupervised both model and data agnostic methodology that can be leveraged for general model fairness discovery. The proposed methodology is tested against a variety of benchmark DL architectures and in the FL environment, showing an average 8.75% increase in Federated model accuracy in comparison with similar work.

Privacy‐preserving and fine‐grained data sharing for resource‐constrained healthcare CPS devices

AbstractMedical cyber‐physical systems (CPS) provide the possibility for real‐time health monitoring of patients and flexible diagnostic services based on expert systems by collaboratively integrating and connecting various physical devices including sensors, terminals, and cloud infrastructure. However, the ubiquitous security threats in cyberspace have raised concerns about data security and user privacy. Although related works propose to protect data security and user privacy with cryptographic protocols, their heavy computational and storage overheads incur performance and battery life challenges for resource‐constrained devices in the healthcare CPS. This article proposes an energy‐saving and privacy‐preserving data sharing (ESPPDS) scheme to address the challenge. ESPPDS inherits the anonymous fine‐grained access control from attribute‐based encryption (ABE) while protecting data integrity and supporting efficient user revocation. We also eliminate the repetitive computations of ciphertext components by utilizing the online/ offline encryption technology, and design a subtle and secure trick to delegate the decryption operations to the edge device, thereby reducing the computational overheads of the resource‐constrained devices. We then show the security proof, and discuss the construction in the untrusted/ compromised server setting. The comparison and experiment indicate that ESPPDS is practical and more efficient than related schemes.

A Provable Secure and Lightweight Smart Healthcare Cyber-Physical System With Public Verifiability

The recent spades of cyber-attacks have compromised patients’ data security and privacy in healthcare cyber-physical systems (CPS) in the era of health 4.0 that integrates the Bio-Medical sensors, Internet of Things (IoT), and cloud technologies. The current research problem with a secure healthcare system for resource-constrained environment is the cost at which a secure healthcare system is achieved. Most existing systems either preserve security and privacy but could not be suitable for a limited resource environment or achieve efficiency but are later found insecure. To solve this issue, this article oversees the equilibrium between the security and efficiency for IoT-enabled secure healthcare platforms. We cryptanalysis Kumar and Chand's system and find their system is insecure due to which the system loses mutual authentication. In this article, we proposed a secure and lightweight smart healthcare CPS system. While preserving the privacy of the system, integrity of patient data on the cloud, and public verifiability of data in the least communication and computation. Additionally, we prove that the proposed solution is secure against indistinguishability under chosen-ciphertext attack and forgeability under chosen message attack.

A real time health care cyber attack detection using ensemble classifier

Healthcare has been a significant issue for many individuals in recent years. The Internet of things (IoT) and wireless communications may support many medical applications, including early diagnosis and real-time monitoring. Healthcare expenditures may be reduced by using secure and practical strategies to detect life-threatening emergencies in real-time quickly. The aim of the proposed tool is the detection of attacks targeting the Healthcare cyber-physical system. In this method, the wise greedy routing technique is initially used for sensor node placement and creation. The transmitted data is normalized and grouped using an agglomerative mean shift maximization clustering method. The abnormal health features are extracted using a multi-heuristic cyber ant optimization-based feature extraction process. Then the attack is detected by using the Ensemble crossover XG boost classifier. An optimized gradient tree boosting system creates decision trees in a sequential form. From the results, the concluded proposed system effectively monitors patient health conditions, detects data breaches, and improves cloud security. Simulated results show that our proposed strategy improves detection accuracy, increases the proportion of true positives, and reduces false positives by a significant margin.

Lattice-Based Self-Enhancement Authorized Accessible Privacy Authentication for Cyber-Physical Systems

Healthcare cyber-physical system significantly facilitates healthcare services and patient treatment effectiveness by analyzing patients’ health information data conveniently. Nevertheless, it also develops the threats to the confidentiality of health information, patients’ privacy, and decidability of medical disputes. And, with the advances of quantum computing technology, most existing anonymous authentication schemes are becoming a growing threat to traditional cryptosystems. To address these problems, for healthcare cyber-physical systems, we propose a new lattice-based self-enhancement authorized accessible privacy authentication scheme by using a strong designated verifier double-authentication-preventing signature technique, called SEAPA. The SEAPA achieves three security and privacy requirements including unforgeability, anonymity for patients’ information, and self-enhancement for patients themselves. A detailed security proof shows our proposal achieves those required security goals. Finally, our construction is demonstrated by parameter analysis and performance evaluation to have reasonable efficiency.

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19.

The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work, we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called “CoviLearn”) to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, and X-rays images can be used to analyze the health of a patient’s lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 98.98% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.

Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality. The privacy of health data can only be preserved by keeping it in an encrypted form, but it affects usability and flexibility in terms of effective search. Attribute-based searchable encryption (ABSE) has proven its worth by providing fine-grained searching capabilities in the shared cloud storage. However, it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations. In a healthcare cloud-based cyber-physical system (CCPS), the data is often collected by resource-constraint devices; therefore, here also, we cannot directly apply ABSE schemes. In the proposed work, the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network. Thus, it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS. With the assistance of blockchain technology, the proposed scheme offers two main benefits. First, it is free from a trusted authority, which makes it genuinely decentralized and free from a single point of failure. Second, it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network. Specifically, the task of initializing the system, which is considered the most computationally intensive, and the task of partial search token generation, which is considered as the most frequent operation, is now the responsibility of the consensus nodes. This eliminates the need of the trusted authority and reduces the burden of data users, respectively. Further, in comparison to existing decentralized fine-grained searchable encryption schemes, the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users. It has been verified both theoretically and practically in the performance analysis section.

Everything You Wanted to Know About Continuous Glucose Monitoring

Diabetes is a chronicle disease where the body of a human is irregular to dissolve the blood glucose properly. The diabetes is due to lack of insulin in human body. The continuous monitoring of blood glucose is main important aspect for health care. Most of the successful glucose monitoring devices is based on methodology of pricking of blood. However, such kind of approach may not be advisable for frequent measurement. The paper presents the extensive review of glucose measurement techniques. The paper covers various non-invasive glucose methods and its control with smart healthcare technology. To fulfill the imperatives for non-invasive blood glucose monitoring system, there is a need to configure an accurate measurement device. Noninvasive glucose-level monitoring device for clinical test overcomes the problem of frequent pricking for blood samples. There is requirement to develop the Internet-Medical-Things (IoMT) integrated Healthcare Cyber-Physical System (H-CPS) based Smart Healthcare framework for glucose measurement with purpose of continuous health monitoring. The paper also covers selective consumer products along with selected state of art glucose measurement approaches. The paper has also listed several challenges and open problems for glucose measurement.

CoviChain: A Blockchain Based Framework for Nonrepudiable Contact Tracing in Healthcare Cyber-Physical Systems During Pandemic Outbreaks.

With the world facing the new virus SARS-CoV-2, many countries have introduced instant Internet applications to identify people carrying the infection. Internet-of-Medical-Things (IoMT) have proven useful in collecting medical data as well in tracing an individual carrying the virus. The data collected or traced belongs to an individual and should be revealed to themselves and hospital providers, but not to any third-party unauthorized agencies. In this paper we use an off-chain distributed storage solution for loading large medical data sets and a blockchain implementation to securely transfer the data from the infected person to the hospital system using the edge infrastructure, and call it CoviChain. The Coronavirus Disease (COVID-19) statistics are loaded on to the edge, and moved to InterPlanetary File Systems (IPFS) storage to retrieve the hash of the data file. Once the hash is obtained, it is moved to the blockchain by means of smart contracts. As the information is being hashed twice, CoviChain addresses the security and privacy issues and avoid exposing individuals’ data while achieving larger data storage on the blockchain with reduced cost and time.

Healthcare Cyber-Physical System is More Important Than Before

Healthcare Cyber-Physical System is More Important Than Before

Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation.

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over (for the first dataset) and (for the second dataset) by labeling only of unlabeled data.

Trustworthy Delegation Toward Securing Mobile Healthcare Cyber-Physical Systems

Attribute-based encryption (ABE) offers a promising solution for flexible access control over sensitive personal health records in a mobile healthcare system on top of a public cloud infrastructure. However, ABE cannot be simply applied to lightweight devices due to its substantial computation cost during decryption. This problem could be alleviated by delegating significant parts of the decryption operations to computationally powerful parties, such as cloud servers, but the correctness of the delegated computation would be at stake. Thus, previous works enabled users to validate the partial decryption by employing a cryptographic commitment or message authentication code (MAC). This paper demonstrates that the previous commitment or MAC-based schemes cannot support verifiability in the presence of potentially malevolent cloud servers. We propose two concrete attacks on previous commitment or MAC-based schemes. We propose an effective countermeasure scheme for securing resource-limited mobile healthcare systems and provide a rigorous security proof in the standard model, demonstrating that the proposed scheme is secure against our attacks. The experimental analysis shows that the proposed scheme provides the similar performance compared with the previous commitment-based schemes and outperforms the MAC-based scheme.