From Conventional to State-of-the-Art IoT Access Control Models

open access articleThe advent in Online Social Networks (OSN) and Internet of Things (IoT) has created a new world of collaboration and communication between people and devices. The domain of internet of things uses billions of devices (ranging from tiny sensors to macro scale devices) that continuously produce and exchange huge amounts of data with people and applications. Similarly, more than a billion people are connected through social networking sites to collaborate and share their knowledge. The applications of IoT such as smart health, smart city, social networking, video surveillance and vehicular communication are quickly evolving people’s daily lives. These applications provide accurate, information-rich and personalized services to the users. However, providing personalized information comes at the cost of accessing private information of users such as their location, social relationship details, health information and daily activities. When the information is accessible online, there is always a chance that it can be used maliciously by unauthorized entities. Therefore, an effective access control mechanism must be employed to ensure the security and privacy of entities using OSN and IoT services. Access control refers to a process which can restrict user’s access to data and resources. It enforces access rules to grant authorized users an access to resources and prevent others. This survey examines the increasing literature on access control for traditional models in general, and for OSN and IoT in specific. Challenges and problems related to access control mechanisms are explored to facilitate the adoption of access control solutions in OSN and IoT scenarios. The survey provides a review of the requirements for access control enforcement, discusses several security issues in access control, and elaborates underlying principles and limitations of famous access control models. We evaluate the feasibility of current access control models for OSN and IoT and provide the future development direction of access control for the sam

Real-time adaptive stochastic control of smart grid data traffic for security purposes

Smart grid data traffic behaves in a similar way to computer network data traffic and they are vulnerable to the same security risks. This paper presents a methodology for the real-time determination of adaptive estimates of router traffic-rate demand every five-minutes, as well as for the evolution of the estimated demand starting from zero during a five-minute interval using a Modified Mean Reverting Stochastic Process (M-MRSP). The determination of real-time adaptive estimates is based on an autoregressive model (AR(n)), which uses a window-size of past real-time router traffic-rate data with coefficients determined by a Kalman Filter (KF). The benefit of this technique is that potential monitoring tools could be provided with future knowledge of one 5-minute interval ahead. The methodology simulations show that the range of RMS error between the KF prediction and the internet service provider (ISP) measurements is of the order of 0.03, whereas the range of RMS error between the KF prediction and the M-MRSP values is of the order of 0.07. The synthetic time-series is a combination of M-MRSP and KF methodologies and maintains all the characteristics of a real traffic-rate time-series, such as non-stationarity, non-normality, long-range dependency (LRD), self-similarity and multimodality. © 2020 Elsevier Lt