ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be very helpful for the prediction of some phenomena, such as chances of getting a heart attack and possibility of a tumor, from the collected and analysed healthcare data. To mitigate these issues, in this paper, a new AI-enabled lightweight, secure communication scheme for an IoMT environment has been designed and named as ASCP-IoMT, in short. The security analysis of ASCP-IoMT is performed in different ways, such as an informal way and a formal way (through the random oracle model). ASCP-IoMT performs better than other similar schemes and provides superior security with extra functionality features as compared those for the existing state of art solutions. A practical implementation of ASCP-IoMT is also performed in order to measure its impact on various network performance parameters. The end to end delay values of ASCP-IoMT are 0.01587, 0.07440 and 0.17097 seconds and the throughput values of ASCP-IoMT are 5.05, 10.88 and 16.41 bits per second (bps) under the different considered cases, respectively. For AI-based Big data analytics phase, the values of computation time (seconds) for decision tree, support vector machine (SVM), and logistic regression are measured as 0.19, 0.23, and 0.27, respectively. Moreover, the different values of accuracy for decision tree, SVM and logistic regression are 84.24%, 87.57%, and 85.20%, respectively. From these values, it is clear that decision tree method requires less time than the other considered techniques, whereas accuracy is high in case of SVM

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

Internet of Medical Things (IoMT ) refers to the network of medical devices and healthcare systems that are connected to the internet. However, this connectivity also makes IoMT vulnerable to cyberattacks such as DoS and Delay attacks , posing risks to patient safety, data security, and public trust. Early detection of these attacks is crucial to prevent harm to patients and system malfunctions. In this paper, we address the detection and mitigation of DoS and Delay attacks in the IoMT using machine learning techniques. To achieve this objective, we constructed an IoMT network scenario using Omnet++ and recorded network traffic data. Subsequently, we utilized this data to train a set of common machine learning algorithms. Additionally, we proposed an Enhanced Random Forest Classifier for Achieving the Best Execution Time (ERF-ABE), which aims to achieve high accuracy and sensitivity as well as  low execution time for detecting these types of attacks in IoMT networks. This classifier combines the strengths of random forests with optimization techniques to enhance performance. Based on the results, the execution time has been reduced by implementing ERF-ABE, while maintaining high levels of accuracy and sensitivity

Cybersecurity in health sector: a systematic review of the literature

Currently, health centers are being affected by various cyberattacks putting at risk the confidential information of their patients and the organization because they do not have a plan or tools to help them mitigate these cyberattacks, which is important to know what measures to take to protect the privacy of personal data. The present work was carried out under a systematic literature review, which aims to show the importance of cybersecurity in the health sector knowing which tools are the most used and efficient to prevent a cyberattack. A systematic review of 301 articles was carried out, 79 of which are aligned with the objective set, fulfilling the inclusion and exclusion criteria. The search for information was carried out in the Scopus and Dimensions databases. The analysis carried out has resulted in good information that was compiled for the development of this topic, being favorable thanks to the different research of different authors

A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases.Comment: 20 pages, 6 tables, 3 figure

Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System †

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97–99.5% based on the F1 scores.Peer reviewe

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.Comment: 29 pages, 7 figures, 2 tables. IEEE Open Journal of the Communications Society (2022

Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Lt

Towards Authentication of IoMT Devices via RF Signal Classification

The increasing reliance on the Internet of Medical Things (IoMT) raises great concern in terms of cybersecurity, either at the device’s physical level or at the communication and transmission level. This is particularly important as these systems process very sensitive and private data, including personal health data from multiple patients such as real-time body measurements. Due to these concerns, cybersecurity mechanisms and strategies must be in place to protect these medical systems, defending them from compromising cyberattacks. Authentication is an essential cybersecurity technique for trustworthy IoMT communications. However, current authentication methods rely on upper-layer identity verification or key-based cryptography which can be inadequate to the heterogeneous Internet of Things (IoT) environments. This thesis proposes the development of a Machine Learning (ML) method that serves as a foundation for Radio Frequency Fingerprinting (RFF) in the authentication of IoMT devices in medical applications to improve the flexibility of such mechanisms. This technique allows the authentication of medical devices by their physical layer characteristics, i.e. of their emitted signal. The development of ML models serves as the foundation for RFF, allowing it to evaluate and categorise the released signal and enable RFF authentication. Multiple feature take part of the proposed decision making process of classifying the device, which then is implemented in a medical gateway, resulting in a novel IoMT technology.A confiança crescente na IoMT suscita grande preocupação em termos de cibersegurança, quer ao nível físico do dispositivo quer ao nível da comunicação e ao nível de transmissão. Isto é particularmente importante, uma vez que estes sistemas processam dados muito sensíveis e dados, incluindo dados pessoais de saúde de diversos pacientes, tais como dados em tempo real de medidas do corpo. Devido a estas preocupações, os mecanismos e estratégias de ciber-segurança devem estar em vigor para proteger estes sistemas médicos, defendendo-os de ciberataques comprometedores. A autenticação é uma técnica essencial de ciber-segurança para garantir as comunicações em sistemas IoMT de confiança. No entanto, os métodos de autenticação atuais focam-se na verificação de identidade na camada superior ou criptografia baseada em chaves que podem ser inadequadas para a ambientes IoMT heterogéneos. Esta tese propõe o desenvolvimento de um método de ML que serve como base para o RFF na autenticação de dispositivos IoMT para melhorar a flexibilidade de tais mecanismos. Isto permite a autenticação dos dispositivos médicos pelas suas características de camada física, ou seja, a partir do seu sinal emitido. O desenvolvimento de modelos de ML serve de base para o RFF, permitindo-lhe avaliar e categorizar o sinal libertado e permitir a autenticação do RFF. Múltiplas features fazem parte do processo de tomada de decisão proposto para classificar o dispositivo, que é implementada num gateway médico, resultando numa nova tecnologia IoMT