Energy-Based Detection of Defect Injection Attacks in IoT-Enabled Manufacturing
Sergio A. Salinas Monroy, Ming Li, Pan Li · 2018
Manufacturing systems are rapidly adopting the Internet of Things (IoT) to improve their efficiency and productivity. The IoT equips manufacturing systems with sensing, computing and communications capabilities, which enable real-time monitoring and control of increasingly complex and geographically distributed factory floors. However, the increased used of computer networks in IoT-enabled manufacturing introduces cyber-vulnerabilities that can be exploited by sophisticated adversaries to sabotage manufacturing operations. A particularly serious cyberattack against manufacturing systems is the defect injection (DI) attack. In a DI attack, a compromised machine fabricates objects with deformed geometry, weak material composition, abnormal dimensions, etc., which pose a great risk to safety-critical applications. In this paper, we develop a method to identify compromised machines that launch DI attacks against smart manufacturing systems. Specifically, we first propose a DI attack localization (DIAL) algorithm that uses machines' energy consumption and voltage measurements to identify compromised machines in the system. Our proposed approach only requires modest hardware resources and can be used in large-scale systems. We implement our DIAL algorithm on a real-world advanced manufacturing testbed, and observe that it can successfully locate the compromised machines with a high detection rate.