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A Maximum-Impact Cyber Adversary Design and Impact Evaluation Framework for Network-Based Control Systems in Smart Buildings
Abstract
Modern building operation and management largely relied on the Internet of Things (IoT) technology by connecting controllable devices through a communication network. However, this all-connected configuration could also expose the network-based control system to cyber-attacks. Part of previous research efforts were spent on modeling and evaluation of the impact of cyber-attacks on network-based building energy and control systems through simulations. One of major assumptions in their model-based cyber-attack designs is that cyber-attacks are arbitrarily injected to the targeted system from a set of predefined adversaries. The limitation of this type of adversary design is that these attacks may be easily detected from the existing state-of-the-art building Automatic Fault Detection and Diagnosis (AFDD) programs without extra effort and thus may be prevented immediately. To address this limitation, this research proposes a maximum-impact cyber-attack design so that the hypocritical adversary can provide the most impactful damage on the controlled system in term of energy efficiency while remaining stealthily hidden from building operators or typical AFDD algorithms. This framework is numerically demonstrated on a network-based building energy and control system. The building energy system is modeled in a Modelica-based simulation environment and controlled by the state-of-the-art control sequences from the ASHRAE Guideline 36. The control commands are assumed to be sent to local controlled through communication networks following the Building Automation and Control Network (BACnet) protocol. The proposed framework generates cyber-attacks for individual and combined targets. The results show that protection priority should be given to condenser water temperature setpoint, air handler unit’s supply air temperature setpoint, and core zone supply air flow rate setpoint for the studied system.
Subject
Cyber-attacksNetwork-based control systems, Building automation systems
Smart buildings
Simulation
Modelica.
Citation
Chu, Mengyuan (2023). A Maximum-Impact Cyber Adversary Design and Impact Evaluation Framework for Network-Based Control Systems in Smart Buildings. Master's thesis, Texas A&M University. Available electronically from https : / /hdl .handle .net /1969 .1 /199037.