Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model.

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg-Marquardt (LM), Conjugate Gradient with Fletcher-Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children's hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.

Spectrum of Advancements and Developments in Multidisciplinary Domains for Generative Adversarial Networks (GANs).

The survey paper summarizes the recent applications and developments in the domain of Generative Adversarial Networks (GANs) i.e. a back propagation based neural network architecture for generative modeling. GANs is one of the most highlighted research avenue due to its synthetic data generation capabilities and benefits of representations comprehended irrespective of the application. While several reviews for GANs in the arena of image processing have been conducted by present but none have given attention on the review of GANs over multi-disciplinary domains. Therefore, in this survey, use of GAN in multidisciplinary applications areas and its implementation challenges have been done by conducting a rigorous search for journal/research article related to GAN and in this regard five renowned journal databases i.e. "ACM Digital Library"," Elsevier", "IEEE Explore", "Science Direct", "Springer" and proceedings of best domain specific conference are considered. By employing hybrid research methodology and article inclusion and exclusion criteria, 100 research articles are considered encompassing 23 application domains for the survey. In this paper applications of GAN in various practical domain and their implementation challenges its associated advantages and disadvantages have been discussed. For the first time a survey of this type have been done where GAN with wide range of application and its associated advantages and disadvantages issue have been reviewed. Finally, this article presents several diversified prominent developing trends in the respective research domain which will provide a visionary perspective regarding ongoing GANs related research and eventually help to develop an intuition for problem solving using GANs.

A First Look at Privacy Analysis of COVID-19 Contact-Tracing Mobile Applications.

Today's smartphones are equipped with a large number of powerful value-added sensors and features, such as a low-power Bluetooth sensor, powerful embedded sensors, such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways, such as tracking the health of the patients and the movement of doctors, tracking employees movement in large manufacturing units, monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people's thoughts to have symptoms of certain diseases or viruses, e.g., COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking, and isolating the persons showing COVID-19 symptoms to limit the spread of the disease to the larger community. The use of embedded sensors could disclose private information of the users, thus potentially bring a threat to the privacy and security of users. In this article, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices are transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.