Predicting context-aware activities using machine-learning techniques is evolving to become more readily available as a major driver of the growth of IoT applications to match the needs of the future smart autonomous environments. However, with today’s increasing security risks in the emerging cloud technologies, which share massive data capabilities and impose regulation requirements on privacy, as well as the emergence of new multiuser, multiprofile, and multidevice technologies, there is a growing need for new approaches to address the new challenges of autonomous context awareness and its fine-grained security-enforcement models. The solutions proposed in this work aim to extend our previous LCA-ABE work to provide an intelligent, dynamic creation of context-aware policies, which has been achieved through deploying smart-learning techniques. It also provides data consent, automated access control, and secure end-to-end communications by leveraging attribute-based encryption (ABE). Moreover, our policy-driven orchestration model is able to achieve an efficient, real-time enforcement of authentication and authorization (AA) as well as federation services between users, service providers, and connected devices by aggregating, modelling, and reasoning context information and then updating consent accordingly in autonomous ways. Furthermore, our framework ensures that the accuracy of our algorithms is above 90% and their precision is around 85%, which is considerably high compared to the other reviewed approaches. Finally, the solution fulfills the newly imposed privacy regulations and leverages the full power of IoT smart environments.
The notion of Context-Awareness of mobile applications is drawing more attention, where many applications need to adapt to physical environments of users and devices, such as location, time, connectivity, resources, etc. While these adaptive features can facilitate better communication and help users to access their information anywhere at any time, this however bring risks caused by the potential loss, misuse, or leak of users' confidential information. Therefore, a flexible policy-based access control system is needed to monitor critical functions executed by Android applications, especially, those requiring access to user's sensitive and crucial information. This paper introduces CAPEF, which is a policy specification framework that enforces context-aware inter-app security policies to mitigate privacy leakage across different Android applications. It also, provides an instrumentation framework to effectively enforce different behaviors based on automated context-aware policies to each Android application individually without modifying the underlying platform. Accordingly, the modified applications will be forced to communicate with our centralized policy engine to avoid any malware collusion that occur without the users' awareness. Experiments conducted on CAPEF shows an effective performance on the size of the enforced application after the instrumentation. The average size added was 705 bytes, which is about 0.063% of the size of the original applications, which is significantly small compared to other existing enforcement approaches. Also, we have denoted that the size and the execution time of the policy increases whenever the policies become more complex.
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