Evaluation of Attribute-Based Access Control (ABAC) for EHR in Fog Computing Environment

Fog computing - a connection of billions of devices nearest to the network edge- was recently proposed to support latency-sensitive and real time applications. Electronic Medical Record (EMR) systems are latency-sensitive in nature therefore fog computing considered as appropriate choice for it. This paper proposes a fog environment for E-health system that contains highly confidential information of patients Electronic Health Records (EHR). The proposed E-health system has two main goals: (1) Manage and share EHRs between multiple fog nodes and the cloud,(2) Secure access into EHR on Fog computing without effecting the performance of fog nodes. This system will serve different users based on their attributes and thus providing Attribute Based Access Control ABAC into the EHR in fog to prevent unauthorized access. We focus on reducing the storing and processes in fog nodes to support low capabilities of storage and computing of fog nodes and improve its performance. There are three major contributions in this paper first; a simulator of an E-health system is implemented using both iFogSim and our iFogSimEhealthSystem simulator. Second, the ABAC was applied at the fog to secure the access to patients EHR. Third, the performance of the proposed securing access in E-health system in fog computing was evaluated. The results showed that the performance of fog computing in the secure E-health system is higher than the performance of cloud computing

Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks

A revolutionary concept of Multi-controller Software-Defined Networking (MC-SDN) is a promising structure for pursuing an evolving complex and expansive large-scale modern network environment. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment against potential vulnerabilities that lead to misuse and malicious activities on data planes. The security holes in the MC-SDN significantly impact network survivability, and subsequently, the data plane is vulnerable to potential security threats and unintended consequences. Accordingly, this work intends to design a Federated learning-based Security (FedSec) strategy that detects the MC-SDN attack. The FedSec ensures packet routing services among the nodes by maintaining a flow table frequently updated according to the global model knowledge. By executing the FedSec algorithm only on the network-centric nodes selected based on importance measurements, the FedSec reduces the system complexity and enhances attack detection and classification accuracy. Finally, the experimental results illustrate the significance of the proposed FedSec strategy regarding various metrics

Navigating Virtual Environments Using Leg Poses and Smartphone Sensors

Realization of navigation in virtual environments remains a challenge as it involves complex operating conditions. Decomposition of such complexity is attainable by fusion of sensors and machine learning techniques. Identifying the right combination of sensory information and the appropriate machine learning technique is a vital ingredient for translating physical actions to virtual movements. The contributions of our work include: (i) Synchronization of actions and movements using suitable multiple sensor units, and (ii) selection of the significant features and an appropriate algorithm to process them. This work proposes an innovative approach that allows users to move in virtual environments by simply moving their legs towards the desired direction. The necessary hardware includes only a smartphone that is strapped to the subjects’ lower leg. Data from the gyroscope, accelerometer and campus sensors of the mobile device are transmitted to a PC where the movement is accurately identified using a combination of machine learning techniques. Once the desired movement is identified, the movement of the virtual avatar in the virtual environment is realized. After pre-processing the sensor data using the box plot outliers approach, it is observed that Artificial Neural Networks provided the highest movement identification accuracy of 84.2% on the training dataset and 84.1% on testing dataset

Proposing a New Hybrid Controlled Loop

Counter controlled and condition controlled are main categories of iterative statements, with “for”, “while ” and “do while ” loops dominating the field. While there are clear distinctions between them, it is not uncommon for a programmer to practically convert a condition controlled loop to do counter controlled task or vice versa. For instance, there is a “while loop ” that will be iterated a number of times or a “for loop” with an additional condition next to the counter checker. This paper proposes a new loop that will be both counter and condition controlled. The loop will include an in-built predefined counter and it will be able to iterate a user defined number of time, prior to checking the user defined condition. A detailed explanation and a C++ implementation of the proposed loop are included in this paper

SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives

Software Product Lines (SPLs) can aid modern ecosystems by rapidly developing large-scale software applications. SPLs produce new software products by combining existing components that are considered as features. Selection of features is challenging due to the large number of competing candidate features to choose from, with different properties, contributing towards different objectives. It is also a critical part of SPLs as they have a direct impact on the properties of the product. There have been a number of attempts to automate the selection of features. However, they offer limited flexibility in terms of specifying objectives and quantifying datasets based on these objectives, so they can be used by various selection algorithms. In this research we introduce a novel feature selection approach that supports multiple multi-level user defined objectives. A novel feature quantification method using twenty operators, capable of treating text-based and numeric values and three selection algorithms called Falcon, Jaguar, and Snail are introduced. Falcon and Jaguar are based on greedy algorithm while Snail is a variation of exhaustive search algorithm. With an increase in 4% execution time, Jaguar performed 6% and 8% better than Falcon in terms of added value and the number of features selected

Automatic Association of Scents Based on Visual Content

Although olfaction can enhance the user’s experience in virtual environments, the approach is not widely utilized by virtual contents. This is because the olfaction displays are either not aware of the content in the virtual world or they are application specific. Enabling wide context awareness is possible through the use of image recognition via machine learning. Screenshots from the virtual worlds can be analyzed for the presence of virtual scent emitters, allowing the olfactory display to respond by generating the corresponding smells. The Convolutional Neural Network (CNN), using Inception Model for image recognition was used for training the system. To evaluate the performance of the accuracy of the model, we trained it on a computer game called Minecraft. The results and performance of the model was 97% accurate, while in some cases the accuracy reached 99%