Biblio

In this research we have explained to set up an Infrared Array Sensor system that is IOT based in order to provide security at remote location. We have tried to Establishment of cloud environment to host IOT application & Development of IOT Application using Asp.net with C\# programming platform. We have Integrated IOT with Infrared Array sensors in order to implement proposed work. In this research camera captures the external event and sent signal to Infrared grid array sensor. Internet of Things (IoT) would enable applications of utmost societal value including smart cities, smart grids & smart healthcare. For majority of such applications, strict dependability requirements are placed on IOT performance, & sensor data as well as actuator commands must be delivered reliably & timely.

Modern infrastructure is heavily reliant on systems with interconnected computational and physical resources, named Cyber-Physical Systems (CPSs). Hence, building resilient CPSs is a prime need and continuous monitoring of the CPS operational health is essential for improving resilience. This paper presents a framework for calculating and monitoring of health in CPSs using data driven techniques. The main advantages of this data driven methodology is that the ability of leveraging heterogeneous data streams that are available from the CPSs and the ability of performing the monitoring with minimal a priori domain knowledge. The main objective of the framework is to warn the operators of any degradation in cyber, physical or overall health of the CPS. The framework consists of four components: 1) Data acquisition and feature extraction, 2) state identification and real time state estimation, 3) cyber-physical health calculation and 4) operator warning generation. Further, this paper presents an initial implementation of the first three phases of the framework on a CPS testbed involving a Microgrid simulation and a cyber-network which connects the grid with its controller. The feature extraction method and the use of unsupervised learning algorithms are discussed. Experimental results are presented for the first two phases and the results showed that the data reflected different operating states and visualization techniques can be used to extract the relationships in data features.

Wearable devices for fitness tracking and health monitoring have gained considerable popularity and become one of the fastest growing smart devices market. More and more companies are offering integrated health and activity monitoring solutions for fitness trackers. Recently insurances are offering their customers better conditions for health and condition monitoring. However, the extensive sensitive information collected by tracking products and accessibility by third party service providers poses vital security and privacy challenges on the employed solutions. In this paper, we present our security analysis of a representative sample of current fitness tracking products on the market. In particular, we focus on malicious user setting that aims at injecting false data into the cloud-based services leading to erroneous data analytics. We show that none of these products can provide data integrity, authenticity and confidentiality.

Wearable and mobile medical devices provide efficient, comfortable, and economic health monitoring, having a wide range of applications from daily to clinical scenarios. Health data security becomes a critically important issue. Electrocardiogram (ECG) has proven to be a potential biometric in human recognition over the past decade. Unlike conventional authentication methods using passwords, fingerprints, face, etc., ECG signal can not be simply intercepted, duplicated, and enables continuous identification. However, in many of the studies, algorithms developed are not suitable for practical application, which usually require long ECG data for authentication. In this work, we introduce a two-phase authentication using artificial neural network (NN) models. This algorithm enables fast authentication within only 3 seconds, meanwhile achieves reasonable performance in recognition. We test the proposed method in a controlled laboratory experiment with 50 subjects. Finger ECG signals are collected using a mobile device at different times and physical statues. At the first stage, a ``General'' NN model is constructed based on data from the cohort and used for preliminary screening, while at the second stage ``Personal'' NN models constructed from single individual's data are applied as fine-grained identification. The algorithm is tested on the whole data set, and on different sizes of subsets (5, 10, 20, 30, and 40). Results proved that the proposed method is feasible and reliable for individual authentication, having obtained average False Acceptance Rate (FAR) and False Rejection Rate (FRR) below 10% for the whole data set.

Detecting early trends indicating cognitive decline can allow older adults to better manage their health, but current assessments present barriers precluding the use of such continuous monitoring by consumers. To explore the effects of cognitive status on computer interaction patterns, the authors collected typed text samples from older adults with and without pre-mild cognitive impairment (PreMCI) and constructed statistical models from keystroke and linguistic features for differentiating between the two groups. Using both feature sets, they obtained a 77.1 percent correct classification rate with 70.6 percent sensitivity, 83.3 percent specificity, and a 0.808 area under curve (AUC). These results are in line with current assessments for MC–a more advanced disease–but using an unobtrusive method. This research contributes a combination of features for text and keystroke analysis and enhances understanding of how clinicians or older adults themselves might monitor for PreMCI through patterns in typed text. It has implications for embedded systems that can enable healthcare providers and consumers to proactively and continuously monitor changes in cognitive function.