A secure cloud framework for ICMetric based IoT health devices.

Wearable devices are an important part of internet of things (IoT)with many applications in healthcare. Prevalent security concerns create a compelling case for a renewed approach by incorporating the ICMetric technology in IoT healthcare. The ICMetric technology is a novel security approach and uses the features of a device to form the basis of cryptographic services like key generation, authentication and admission control. Cryptographic systems designed using ICMetric technology use unique measurable device features to form a root of trust. This paper uses the MEMS bias in a body wearable Shimmer sensor to create a device ICMetric. The ICMetric identity is used to generate cryptographic key to perform encryption and decryption of patients data which is being communicated to health professionals. The cloud based component of the proposed framework provides much needed distributed data processing and availability. The proposed schemes have been simulated and tested for conformance to high levels of security and performance

Icmapen: an icmetric based security framework for sleep apnea monitoring

Smart devices are becoming increasingly powerful which is why they are being used for point of care health services. Wearable devices can be purchased which allow continuous monitoring of a wearers vital signs. The data is generated, processed and stored remotely where it can be readily accessible to health professionals. Recent attacks on healthcare systems and health data shows that the systems are insecure and that security is a major hurdle in their wide adoption. Conventional cryptographic systems rely on stored keys for the provision of security. The stored keys can be captured in many ways which leads to the system being exposed. The ICMetric technology remedies this by eliminating the need for stored keys. Thus, the ICMetric technology functions as a key theft deterrent and as a basis for cryptographic services. This paper studies the design and implementation of an ICMetric based health monitoring system for people diagnosed with sleep apnea. The proposed system provides key generation, authentication and confidentiality by using the novel ICMetric technology. The proposed scheme is constituent of a cloud computing component which enables remote monitoring and data storage for access by health professionals.  This paper studies the performance of the proposed schemes by studying the running time. The security of the scheme has also been studied to show that the system provides high levels of security without resource compromise.Keywords: ICMetric; Sleep apnea; Cloud computing; Authentication; Confidentialit

An ICMetric based multiparty communication framework

Cryptographic algorithms have always relied on stored keys for the provision of security services. Since these keys are stored on a system this makes them prone to attack. Efforts to increase the key size makes brute forcing difficult but does not eliminate key theft. This thesis proposes a comprehensive security framework for groups of devices. The research makes four major contributions to improve the security of devices in the multiparty environment. The proposed framework uses the novel Integrated Circuit Metric (ICMetric) technology which proposes utilizing measurable properties and features of a device to create a device identification. This device identification called the ICMetric is used to create cryptographic keys which are then used in the designed cryptosystems. The first contribution of the thesis is the creation of an ICMetric using sensors found in modern smart devices. The research explores both explicit and implicit features which can be used to generate of an ICMetric. The second contribution of this research is the creation of a group ICMetric which is computed using the device ICMetric. The computation of the device ICMetric is a particular challenge as it has to be computed without violating the properties of the ICMetric technology. The third contribution is the demonstration that an ICMetric can be used for the creation of symmetric key. The fourth contribution of this research is an efficient RSA based asymmetric key generation scheme for the multiparty environment. Designing a system using widely accepted cryptographic primitives does not guarantee a secure system therefore the security of proposed schemes has been studied under the standard model. The schemes presented in this thesis attempt to improve the security of devices in the group environment. The schemes demonstrate that key theft deterrent technologies can be incorporated into cryptographic schemes to offer higher levels of security and privacy

SortAlgo-Metrics: Identification of Cloud-Based Server Via a Simple Algorithmic Analysis

This paper introduces a novel technique to detect spoof or fake software systems via the generation of a unique digital signature based on a direct analysis of the construction of the system. Specifically, we model a novel mechanism referred to as SortAlgo-Metrics analysis to identify cloud-based servers. Experimentally, we deployed four cloud-based servers to run four sorting algorithms in order to extract features that are employed to perform statistical analysis upon with the aim to obtain their metrics which has further underpin the investigation of their behaviours. The model has been validated by comparing training data and unknown data, and the result has shown server 2-4 have a strong identification with 96% probability, while server 1 with 55%, it is surmised that is could be as the result of insufficient sample data. However, if such a simple model can produce a result with this high probability, this shows that with more complex features and sufficient data pulled from cloud-based servers, SortAlgo-Metrics model could generate a higher degree of basis numbers for ICMetrics technology entropy key generation and other complex systems

Robust Device Authentication Using Non-Standard Classification Features

This paper investigates the use of novel hardware features derived from the physical and behavioral characteristics of electronic devices to identify such devices uniquely. Importantly, the features examined exhibit non-standard and multimodal distributions which present a significant challenge to model and characterize. Specifically, the potency of four data classification methods is compared whilst employing such characteristics, proposed model Multivariate Gaussian Distribution (MVGD -address multimodality), Logistic Regression (LogR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM). Performance is measured based on its accuracy, precision, recall and f measure. The experimental results reveal that by addressing multimodal features with proposed model Multivariate Gaussian Distribution classifier, the overall performance is better than the other classifiers

On Secure Group Admission Control Using ICMetrics

The security of a system cannot be certified unless there are formal methods of admission control. Many techniques and protocol have been proposed that try to provide security yet do not focus on the most important question about who has access to the system. When considering group communications it is more important to understand this problem as the security of the system is dependent upon having authorized entities in the group communicating securely. Admission control has previously been studied in distributed systems but repeatedly overlooked in security. In this paper we provide a polling centred admission control system based on ICMetrics. We choose the polling based system as it considers the opinion of current group members when giving access to members wishing to join the group. Our proposed protocol is based on the use of the secure ring signature along with the latest ICMetrics technology

Secure device identification using multidimensional mapping

In this paper we investigate several potential hardware features from multiple devices for suitability during the employment of a device identification. The generation of stable and unique digital identity from features is challenging in device identification because of the unstable operation environments that implies the features employed are likely to vary under normal operating conditions. To address this, we introduce a novel multi-dimensional key generation technology which maps from multi-dimensional feature space directly to a key space. Furthermore, normalized distributions of features give the necessary data to model the characteristics, from which we derive intra-sample device feature distributions, and correlate the distinct features to generate a secure key to identify the device. Furthermore, to evaluate our experiment, we considerably carried out measurement using the mathematical & statistical modelling

Device Identification Using Discrete Wavelet Transform

This paper investigates the effectiveness of employing measured hardware features mapped into the frequency domain for devices identification. The technique is to utilize Discrete Wavelet Transform (DWT) coefficients as distinguishing features. The DWT coefficients address the degree of relationship between the investigated features and the wavelet function at different occurrences of time. Therefore, DWT coefficients carry useful temporal information about the transient activity of the investigated wavelet features. We study the impacts of utilizing different wavelet functions (Coiflets, Haar and Symlets) on the performance of the device identification system. This system yields 92.5 % of accuracy using Sym6 wavelet. A comparison is made of the accuracy of wavelet features and raw features with standard classifiers

Interdisciplinary perspectives on privacy awareness in lifelogging technology development

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL (‘Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People’), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies.Open Access funding enabled and organized by Projekt DEAL. This work is part of the PAAL-project (“Privacy-Aware and Acceptable Lifelogging services for older and frail people”). The support of the Joint Programme Initiative “More Years, Better Lives” (award number: PAAL_JTC2017), the German Federal Ministry of Education and Research (grant no: 16SV7955), the Swedish Research Council for Health, Working Life, and Welfare (grant no: 2017–02302), the Spanish Agencia Estatal de Investigacion (PCIN-2017-114), the Italian Ministero dell’Istruzione dell’Universitá e della Ricerca, (CUP: I36G17000380001), and the Canadian Institutes of Health Research is gratefully acknowledged

Development of Novel Big Data Analytics Framework for Smart Clothing

© 2013 IEEE. Recent advances in micro electro-mechanical systems (MEMS) have produced wide variety of wearable sensors. Owing to their low cost, small size and interfacability, those MEMS based devices have become increasingly commonplace and part of daily life for many people. Large amount of data from heart and breath rates to electrocardiograph (ECG) signals, which contain a wealth of health-related information, can be measured. Hence, there is a timely need for novel interrogation and analysis methods for extracting health related features from such a Big Data. In this paper, the prospects from smart clothing such as wearable devices in generating Big Data are critically analyzed with a focus on applications related to healthcare, sports and fashion. The work also covers state-of-the-art data analytics methods and frameworks for health monitoring purposes. Subsequently, a novel data analytics framework that can provide accurate decision in both normal and emergency health situations is proposed. The proposed novel framework identifies and discusses sources of Big Data from the human body, data collection, communication, data storage, data analytics and decision making using artificial intelligence (AI) algorithms. The paper concludes by identifying challenges facing the integration of Big Data analytics with smart clothing. Recommendation for further development opportunities and directions for future work are also suggested