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

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

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

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

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

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

Investigation of Properties of ICmetrics Features

The ICmetrics technology is concerned with identifying acceptable features in an electronic system's operation for encryption purposes. Ideally, the nature of the features should be identical for all of the systems considered, while the values of these features should allow for unique identification of each of the systems. This paper looks at the properties of the Program Counter of a processor core as a potential ICmetrics feature, and explores how the number of its samples being inputted into the ICmetrics system affects stability of the system's performance