Mathematical Modelling of ANP for Trust Based IoT Device Categorization in Secured Smart Logistics

In today’s era of massive information exchange, trust plays a significant role in the Internet of Things (IoT) security and privacy. Trust specifies the confidence of one point or network entity on another in a network model. As IoT devices of smart logistics rely on Internet connectivity, potential cyber-attacks pose a genuine threat. IoT devices have constrained capabilities, and therefore it is very difficult to differentiate between trustworthy and malicious IoT devices. The main contribution of this research is, first, to develop a mathematical model of the Analytical Network Process (ANP) for security and privacy in smart logistics. Second, IoT devices are categorized using trust as an attribute in the security domain of the smart logistics framework. Third, the applicability of ANP as a trust model for malicious IoT device identification is evaluated. And forth, for comparison, the Analytical Hierarchal Process (AHP) is applied to the same trust model for malicious IoT device identification, and results are compared with ANP. The proposed mathematical model of ANP for secured smart logistics is designed using three conceptual facets i.e., goal, criteria, and alternatives. The goal of the trust model is to identify malicious IoT devices using multi-criteria based on three trust parameters, i.e., Entity-Oriented Trust Model (EOTM), Data-Oriented Trust Model (DOTM), and Combined Trust Model (CTM). The alternatives in the trust model are the various candidate IoT devices. The architecture of the smart logistics framework has three modules i.e., internal IoT network, border router, and external network. The IoT devices communicate through the border router with the external world. Therefore, IoT devices are scanned by border router and taken as a trusted node in the secured smart logistics framework. Additionally, for comparison and validation, results obtained from ANP and AHP are evaluated. Results reveal a percentage difference of 16.86% for malicious IoT device identification and 22.10% for most trustworthy IoT device identification on AHP and ANP. In both cases ANP outperforms AHP. For stability, a sensitivity analysis is executed for AHP and ANP, which shows a deviation in terms of the relative standard deviation of 8.4% and 11.05% for malicious and trustworthy IoT device identification using multi-criteria of trust respectively.