Developing an adaptive Risk-based access control model for the Internet of Things

Abstract

The Internet of Things (IoT) is creating a revolution in the number of connected devices. Cisco reported that there were 25 billion IoT devices in 2015 and modest estimation that this number will almost double by 2020. Society has become dependent on these billions of devices, devices that are connected and communicating with each other all the time with information constantly share between users, services, and internet providers.

The emergent IoT devices as a technology are creating a huge security rift between users and usability, sacrificing usability for security created a number of major issues. First, IoT devices are classified under Bring Your Own Device (BYOD) that blows any organization security boundary and make them a target for espionage or tracking. Second, the size of the data generated from IoT makes big data problems pale in comparison not to mention IoT devices need a real-time response. Third, is incorporating secure access and control for IoT devices ranging from edge nodes devices to application level (business intelligence reporting tools) is a challenge because it has to account for several hardware and application levels. Establishing a secure access control model between different IoT devices and services is a major milestone for the IoT. This is important because data leakage and unauthorized access to data have a high impact on our IoT devices. However, traditional access control models with the static and rigid infrastructure cannot provide the required security for the IoT infrastructure.

Therefore, this paper proposes a risk-based access control model for IoT technology that takes into account real-time data information request for IoT devices and gives dynamic feedback. The proposed model uses IoT environment features to estimate the security risk associated with each access request using user context, resource sensitivity, action severity and risk history as inputs for security risk estimation algorithm that is responsible for access decision. Then the proposed model uses smart contracts to provide adaptive features in which the user behaviour is monitored to detect any abnormal actions from authorized users.

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Identifiers

ORCID for Hany F. Atlam: orcid.org/0000-0003-4142-6377

ORCID for Gary B. Wills: orcid.org/0000-0001-5771-4088

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