Enhancing Cybersecurity for Industrial Control Systems: Innovations in Protecting PLC-Dependent Industrial Infrastructures
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97 MATHEMATICS AND COMPUTING↗
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Strengthening Cybersecurity for Industrial Control Systems: Innovations in Protecting PLC-Based Infrastructure
In this paper, we propose two new approaches aimed at enhancing the security of industrial control systems (ICS) that utilize programmable logic controllers (PLCs) for the control of critical processes. The first approach involves the addition of a unique digital watermark to the PWM control that adjusts the motor speed to control the critical process. This enables efficient detection and identification of any unauthorized modifications to the sensor signals responsible for controlling the plant. The second approach focuses on monitoring the input current (i.e power) drawn by the PLC during the execution of critical process control tasks. Malicious intrusions to change the PLC parameters and/or unauthorized firmware updates can be rapidly detected. Both approaches demonstrate a substantial improvement in the security of ICS, effectively safeguarding against potential cyber-attacks. Experimental results from a laboratory scale water tank level controlled via PLC showcases rapid intrusion detection capabilities.
Huang, Peng-Hao↗
PLC Vulnerabilities and Mitigations
Programmable Logic Controllers (PLCs) are used extensively in many high-importance industrial and nonindustrial settings including controlling elevators, manufacturing machines, and utility facilities such as power and natural gas, however cybersecurity protection for them has been neglected. Within recent years, PLCs have been put under more security scrutiny and experts have advocated for changes from the addition of protocol encryption and network segmentation to intrusion detection systems on the PLCs themselves. While PLC security is critical, a large portion of the PLCs available today will never receive these changes due to being legacy or the difficulty of overhauling the security on existing systems. Due to the infeasibility of applying many recommended security measures towards currently available machines, we aim to provide realistic and affordable best practices for hardening PLCs. We will first conduct security analysis and consider attack vectors within our target PLC. Once we’ve analyzed the device’s security, we will evaluate a variety of mitigation methods and create guidelines to effectively reduce the threat posed by PLC attacks with minimal disruption to operations.
42 ENGINEERING↗
Automated Cyber Security Testing Platform for Industrial Control Systems
Nuclear Power Plants (NPPs) are a complex system of coupled physics controlled by a network of Programmable Logic Controllers (PLCs). These PLCs communicate process data across the network to coordinate control actions with each other and inform the operators of process variables and control decisions. Networking the PLCs allows more effective process control and provides the operator more information which results in more efficient plant operation. This interconnectivity creates new security issues, as operators have more access to the plant controls, so will bad actors. As plant networks become more digitized and encompass more sophisticated controllers, the network surface exposed to cyber interference grows. Understanding the dynamics of these coupled systems of physics, control logic, and network communications is critical to their protection. The research into the cybersecurity of the Operational Technologies of NPPs is developing and requires a platform that can allow high fidelity physics simulations to interact with digital networks of controllers. This will require three main components: a network simulation environment, a physics simulator, and virtual PLCs (vPLC) that represent typical industry hardware. A platform that incorporates these three components to provide the most accurate representation of actual NPP networks and controllers is developed in this paper.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Pressurizer dynamic model and emulated programmable logic controllers for nuclear power plants cybersecurity investigations
This work demonstrates the functionality of the pressurizer using a fast-running three regions, non-equilibrium model and control programs of emulated PLCs. The state variables from an integrated model of primary loop in a representative PWR plant are communicated to the pressurizer’s emulated PLCs using a synchronized data transfer function. In turn, the PLCs communicate back instructions to the pressurizer model to adjust the pressure and water level to remain within preprogramed setpoints. Pressurizer model simultaneously solves the coupled mass and energy conservation equations in the saturated vapor and saturated and subcooled liquid regions using fixed step solver. Results demonstrate the response of the pressurizer model linked to an emulated pressure and water level PLCs in a simulated transient involving surge-in and surge-out events. The 50 ms response delay time for the emulated PLC insignificantly affects operation and predictions of the pressurizer model.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Detecting control system misbehavior by fingerprinting programmable logic controller functionality
In recent years, attacks such as the Stuxnet malware have demonstrated that cyberattacks against control systems cause extensive damage. These attacks can result in physical damage to the networked systems under their control. In this paper, we discuss our approach for detecting such attacks by distinguishing between programs running on a programmable logic controller (PLC) without having to monitor communications. Using power signatures generated by an attached, high-frequency power measurement device, we can identify what a PLC is doing and when an attack may have altered what the PLC should be doing. To accomplish this, we generated labeled data for testing our methods and applied feature engineering techniques and machine learning models. The results of this research demonstrate that Random Forests and Convolutional Neural Networks classify programs with up to 98% accuracy for major program differences and 84% accuracy for minor differences. Our results can be used for both online and offline applications.
97 MATHEMATICS AND COMPUTING↗