Adaptive Contextual Risk-Based Model to Tackle Confidentiality-Based Attacks in Fog-IoT Paradigm

Abstract

The Internet of Things (IoT) allows billions of physical objects to be connected to gather and exchange information to offer numerous applications. It has unsupported features such as low latency, location awareness, and geographic distribution that are important for a few IoT applications. Fog computing is integrated into IoT to aid these features to increase computing, storage, and networking resources to the network edge. Unfortunately, it is faced with numerous security and privacy risks, raising severe concerns among users. Therefore, this research proposes a contextual risk-based access control model for Fog-IoT technology that considers real-time data information requests for IoT devices and gives dynamic feedback. The proposed model uses Fog-IoT environment features to estimate the security risk associated with each access request using device context, resource sensitivity, action severity, and risk history as inputs for the fuzzy risk model to compute the risk factor. Then, the proposed model uses a security agent in a fog node to provide adaptive features in which the device’s behaviour is monitored to detect any abnormal actions from authorised devices. The proposed model is then evaluated against the existing model to benchmark the results. The fuzzy-based risk assessment model with enhanced MQTT authentication protocol and adaptive security agent showed an accurate risk score for seven random scenarios tested compared to the simple risk score calculations.