Discrete Chaotic Fuzzy Neural Network (DC-FNN) Based Smart Watch Embedded Devices for Sports and Health Monitoring

Improved athletic performance is expected to result from the convergence of semiconductor technology from the wearable device equipped with physiology and its clinical and translation tools. The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services.This research optimises the usage of smart watch integrated devices through wireless connection, which sheds light on wearable sensors used in sports medicine. The major objective of this article is to provide a recommended method of using wearable technology for evaluating the efficacy of health and sports monitoring. Any sport at any level may stand to profit from this embedded technology, as might academic research labs, sports medicine practises, and professional sports teams all working toward the same goal of improving player and team performance. As the primary data generated by wearable devices include the heartbeat rate, step count, and energy consumed, researchers have concentrated on associating cardiovascular disorders with these data. A Discrete Chaotic Fuzzy Neural Network (DC-FNN) model was presented to analyse smart watch functionality for use in fitness and health tracking. This study used machine learning algorithm for analyzing the performance of wearing smart watch embedded device among sports players. The study employs discrete chaotic Fuzzy neural network for evaluating the recognition time and efficiency of the embedded device. The Discrete Chaotic Fuzzy Neural Network (DC-FNN) theories focus on the expertise and experience of specialists who understand how sports system works in different parameters. The major elements of the DC-FNN strategy are based mostly on expert expertise. This research work highlights how wearable sensors can help players and trainers keep tabs on athletes\u27 biomechanical and physiological health in real time, preventing or delaying the start of injuries and providing a more accurate picture of how they are doing. Athlete involvement risk is mediated by the interplay between tissue health and training

A Framework for Uncertain Cloud Data Security and Recovery Based on Hybrid Multi-User Medical Decision Learning Patterns

Machine learning has been supporting real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also, this cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers to store medical records or machine learning patterns for decision making. Due to high Uncertain computational memory and time, these cloud systems require an efficient data security framework to provide strong data access control among the multiple users. In this work, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases’ i.e. data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for Uncertain data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering, and security framework has better computational efficiency than the conventional approaches on large medical decision patterns

Security for networked smart healthcare systems: A systematic review

Background and Objectives Smart healthcare systems use technologies such as wearable devices, Internet of Medical Things and mobile internet technologies to dynamically access health information, connect patients to health professionals and health institutions, and to actively manage and respond intelligently to the medical ecosystem's needs. However, smart healthcare systems are affected by many challenges in their implementation and maintenance. Key among these are ensuring the security and privacy of patient health information. To address this challenge, several mitigation measures have been proposed and some have been implemented. Techniques that have been used include data encryption and biometric access. In addition, blockchain is an emerging security technology that is expected to address the security issues due to its distributed and decentralized architecture which is similar to that of smart healthcare systems. This study reviewed articles that identified security requirements and risks, proposed potential solutions, and explained the effectiveness of these solutions in addressing security problems in smart healthcare systems. Methods This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines and was framed using the Problem, Intervention, Comparator, and Outcome (PICO) approach to investigate and analyse the concepts of interest. However, the comparator is not applicable because this review focuses on the security measures available and in this case no comparable solutions were considered since the concept of smart healthcare systems is an emerging one and there are therefore, no existing security solutions that have been used before. The search strategy involved the identification of studies from several databases including the Cumulative Index of Nursing and Allied Health Literature (CINAL), Scopus, PubMed, Web of Science, Medline, Excerpta Medical database (EMBASE), Ebscohost and the Cochrane Library for articles that focused on the security for smart healthcare systems. The selection process involved removing duplicate studies, and excluding studies after reading the titles, abstracts, and full texts. Studies whose records could not be retrieved using a predefined selection criterion for inclusion and exclusion were excluded. The remaining articles were then screened for eligibility. A data extraction form was used to capture details of the screened studies after reading the full text. Of the searched databases, only three yielded results when the search strategy was applied, i.e., Scopus, Web of science and Medline, giving a total of 1742 articles. 436 duplicate studies were removed. Of the remaining articles, 801 were excluded after reading the title, after which 342 after were excluded after reading the abstract, leaving 163, of which 4 studies could not be retrieved. 159 articles were therefore screened for eligibility after reading the full text. Of these, 14 studies were included for detailed review using the formulated research questions and the PICO framework. Each of the 14 included articles presented a description of a smart healthcare system and identified the security requirements, risks and solutions to mitigate the risks. Each article also summarized the effectiveness of the proposed security solution. Results The key security requirements reported were data confidentiality, integrity and availability of data within the system, with authorisation and authentication used to support these key security requirements. The identified security risks include loss of data confidentiality due to eavesdropping in wireless communication mediums, authentication vulnerabilities in user devices and storage servers, data fabrication and message modification attacks during transmission as well as while the data is at rest in databases and other storage devices. The proposed mitigation measures included the use of biometric accessing devices; data encryption for protecting the confidentiality and integrity of data; blockchain technology to address confidentiality, integrity, and availability of data; network slicing techniques to provide isolation of patient health data in 5G mobile systems; and multi-factor authentication when accessing IoT devices, servers, and other components of the smart healthcare systems. The effectiveness of the proposed solutions was demonstrated through their ability to provide a high level of data security in smart healthcare systems. For example, proposed encryption algorithms demonstrated better energy efficiency, and improved operational speed; reduced computational overhead, better scalability, efficiency in data processing, and better ease of deployment. Conclusion This systematic review has shown that the use of blockchain technology, biometrics (fingerprints), data encryption techniques, multifactor authentication and network slicing in the case of 5G smart healthcare systems has the potential to alleviate possible security risks in smart healthcare systems. The benefits of these solutions include a high level of security and privacy for Electronic Health Records (EHRs) systems; improved speed of data transaction without the need for a decentralized third party, enabled by the use of blockchain. However, the proposed solutions do not address data protection in cases where an intruder has already accessed the system. This may be potential avenues for further research and inquiry

A lightweight and secure multilayer authentication scheme for wireless body area networks in healthcare system

Wireless body area networks (WBANs) have lately been combined with different healthcare equipment to monitor patients' health status and communicate information with their healthcare practitioners. Since healthcare data often contain personal and sensitive information, it is important that healthcare systems have a secure way for users to log in and access resources and services. The lack of security and presence of anonymous communication in WBANs can cause their operational failure. There are other systems in this area, but they are vulnerable to offline identity guessing attacks, impersonation attacks in sensor nodes, and spoofing attacks in hub node. Therefore, this study provides a secure approach that overcomes these issues while maintaining comparable efficiency in wireless sensor nodes and mobile phones. To conduct the proof of security, the proposed scheme uses the Scyther tool for formal analysis and the Canetti–Krawczyk (CK) model for informal analysis. Furthermore, the suggested technique outperforms the existing symmetric and asymmetric encryption-based schemes

Design and implementation of a multi-modal sensor with on-chip security

With the advancement of technology, wearable devices for fitness tracking, patient monitoring, diagnosis, and disease prevention are finding ways to be woven into modern world reality. CMOS sensors are known to be compact, with low power consumption, making them an inseparable part of wireless medical applications and Internet of Things (IoT). Digital/semi-digital output, by the translation of transmitting data into the frequency domain, takes advantages of both the analog and digital world. However, one of the most critical measures of communication, security, is ignored and not considered for fabrication of an integrated chip. With the advancement of Moore\u27s law and the possibility of having a higher number of transistors and more complex circuits, the feasibility of having on-chip security measures is drawing more attention. One of the fundamental means of secure communication is real-time encryption. Encryption/ciphering occurs when we encode a signal or data, and prevents unauthorized parties from reading or understanding this information. Encryption is the process of transmitting sensitive data securely and with privacy. This measure of security is essential since in biomedical devices, the attacker/hacker can endanger users of IoT or wearable sensors (e.g. attacks at implanted biosensors can cause fatal harm to the user). This work develops 1) A low power and compact multi-modal sensor that can measure temperature and impedance with a quasi-digital output and 2) a low power on-chip signal cipher for real-time data transfer

A Naïve Visual Cryptographic Algorithm for the Transfer of Compressed Medical Images

The transmission of a suitably compressed image over a bandwidth, over long distances, gives rise towards a new era in the field of information technology. A gradual increase in this appending scenic application, involving the transfer of the images securely over the Ethernet has become an increasingly important aspect to be addressed during thou phenomenon, especially in the transfer of the digital medical images vividly, encapsulated with abundant information related to these images. The compressed medical images of the DICOM format contain certain amount of confidential data, pertaining to a clinical research or to an individual, and the confidentiality of the same has to be preserved from various security threats and eves-dropping. With a widespread applications among various multimedia applicative systems, telemedicine, medical imaging, military and certain safety-critical applications, inter-net and intra-net communicative applications, etc, a reliable transfer of suitable information, efficiently & securely is considered as one of the revolutionary aims in today’s communication technology and visual cryptographic methodologies. Real-time applications as such detailed above majorly is concerned with the security measures and many algorithms have been developed as a proof for various visual cryptographic methodologies. In this paper we propose an efficient and a reliable visual cryptographic methodology which focuses on the encryption and decryption of the two-dimensional DICOM standard compressed medical image, effectively.  This paper discusses an efficient design of 192 bit encoder using AES Rijndael Algorithm with the decomposition of an image into square image size blocks and the image blocks are shuffled using 2D CAT map. The shuffling of the image blocks/pixels employs a Logistic map of these image pixels coupled with 2D mapping of the pixels of the DICOM standard medical image, generated randomly, being the control parameter thereby creating a confusion between the cipher and the plain image, gradually increasing the resistive factor against the significant attacks. This paper proposes various analytical metrics such as correlation analysis, entropy analysis, homogeneity analysis, energy analysis, contrast and mean of absolute deviation analysis, to evaluate the proposed algorithm, and their suitability in image encryption applications

An enhanced lossless compression with cryptography hybrid mechanism for ECG biomedical signal monitoring

Due to their use in daily life situation, demand for remote health applications and e-health monitoring equipment is growing quickly. In this phase, for fast diagnosis and therapy, information can be transferred from the patient to the distant clinic. Nowadays, the most chronic disease is cardiovascular diseases (CVDs). However, the storage and transmission of the ECG signal, consumes more energy, bandwidth and data security which is faced many challenges. Hence, in this work, we present a combined approach for ECG data compression and cryptography. The compression is performed using adaptive Huffman encoding and encrypting is done using AES (CBC) scheme with a 256-bit key. To increase the security, we include Diffie-Hellman Key exchange to authenticate the receiver, RSA key generation for encrypting and decrypting the data. Experimental results show that the proposed approach achieves better performance in terms of compression and encryption on MIT-BIH ECG dataset

e-SAFE: Secure, Efficient and Forensics-Enabled Access to Implantable Medical Devices

To facilitate monitoring and management, modern Implantable Medical Devices (IMDs) are often equipped with wireless capabilities, which raise the risk of malicious access to IMDs. Although schemes are proposed to secure the IMD access, some issues are still open. First, pre-sharing a long-term key between a patient's IMD and a doctor's programmer is vulnerable since once the doctor's programmer is compromised, all of her patients suffer; establishing a temporary key by leveraging proximity gets rid of pre-shared keys, but as the approach lacks real authentication, it can be exploited by nearby adversaries or through man-in-the-middle attacks. Second, while prolonging the lifetime of IMDs is one of the most important design goals, few schemes explore to lower the communication and computation overhead all at once. Finally, how to safely record the commands issued by doctors for the purpose of forensics, which can be the last measure to protect the patients' rights, is commonly omitted in the existing literature. Motivated by these important yet open problems, we propose an innovative scheme e-SAFE, which significantly improves security and safety, reduces the communication overhead and enables IMD-access forensics. We present a novel lightweight compressive sensing based encryption algorithm to encrypt and compress the IMD data simultaneously, reducing the data transmission overhead by over 50% while ensuring high data confidentiality and usability. Furthermore, we provide a suite of protocols regarding device pairing, dual-factor authentication, and accountability-enabled access. The security analysis and performance evaluation show the validity and efficiency of the proposed scheme

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security