Threat Modeling for Communication Security of IoT-Enabled Digital Logistics

Junejo, Aisha Kanwal; Breza, Michael; McCann, Julie A.

Abstract

The modernization of logistics through the use of Wireless Sensor Network (WSN) Internet of Things (IoT) devices promises great efficiencies. Sensor devices can provide real-time or near real-time condition monitoring and location tracking of assets during the shipping process, helping to detect delays, prevent loss, and stop fraud. However, the integration of low-cost WSN/IoT systems into a pre-existing industry should first consider security within the context of the application environment. In the case of logistics, the sensors are mobile, unreachable during the deployment, and accessible in potentially uncontrolled environments. The risks to the sensors include physical damage, either malicious/intentional or unintentional due to accident or the environment, or physical attack on a sensor, or remote communication attack. The easiest attack against any sensor is against its communication. The use of IoT sensors for logistics involves the deployment conditions of mobility, inaccesibility, and uncontrolled environments. Any threat analysis needs to take these factors into consideration. This paper presents a threat model focused on an IoT-enabled asset tracking/monitoring system for smart logistics. A review of the current literature shows that no current IoT threat model highlights logistics-specific IoT security threats for the shipping of critical assets. A general tracking/monitoring system architecture is presented that describes the roles of the components. A logistics-specific threat model that considers the operational challenges of sensors used in logistics, both malicious and non-malicious threats, is then given. The threat model categorizes each threat and suggests a potential countermeasure.

Citation

Junejo, A. K., Breza, M., & McCann, J. A. (in press). Threat Modeling for Communication Security of IoT-Enabled Digital Logistics. Sensors, 23(23), Article 9500. https://doi.org/10.3390/s23239500

• Journal Article Type: Article
• Acceptance Date: Nov 11, 2023
• Online Publication Date: Nov 29, 2023
• Deposit Date: Dec 11, 2023
• Journal: Sensors
• Print ISSN: 1424-8220
• Electronic ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 23
• Article Number: 9500
• DOI: https://doi.org/10.3390/s23239500
• Keywords: tracking, attacks, threat model, digital logistics, vulnerabilities, security