SCOPE: Secure Compiling of PLCs in Cyber-Physical Systems

Chekole, Eyasu Getahun; Ochoa, Martín; Chattopadhyay, Sudipta

doi:10.1016/j.ijcip.2021.100431

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…To simulate the above process, we design a mini-CPS testbed using IoT devices, sensors and actuators. Specifically, we use Arduino MEGA 6 as a soft PLC, which makes control 6 https://store.arduino.cc/products/arduino-mega-2560-rev3 decisions based on the temperature reading. We use an analog sensor, called LM35 7 , to read the surrounding temperature.…”

Section: B the Proposed Attacksmentioning

confidence: 99%

“…These includes IoT, control systems (e.g., ICS/CPS), image processing, digital multimeters, cell phones, and medical imaging, to name a few. For example, PLCs [5], [6] in ICS/CPS [7], [8] often make control decisions based on the input obtained from analog sensors (e.g., temperature, pressure and force sensors). However, they cannot directly use the analog input as they cannot understand analog signals.…”

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confidence: 99%

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Exploiting Vulnerabilities of ADC Registers in IoT and Control Systems

Chekole,

Thulasiraman,

Zhou

2023

Preprint

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<p>An analog-to-digital converter (ADC) is a critical part of most computing systems that converts analog signals into quantifiable digital values. Since most digital devices operate only on digital values, the ADC acts as an interface between the digital and analog worlds. As such, ADCs are commonly used in a wide-range of applications, including internet of things (IoT), industrial control systems (ICS), cyber-physical systems (CPS), audio/video devices, medical imaging, digital oscilloscopes, and cell phones, among others. For example, programmable logic controllers (PLCs) in ICS/CPS often make control decisions based on digital values converted from analog signals by ADCs. Due to its crucial role in various applications, ADCs are often targeted by a wide-range of physical and cyber attacks. Attackers often exploit vulnerabilities that could be found in the software/hardware of ADCs. In this work, we first conduct a deeper study in the ADC conversion logic to investigate relevant vulnerabilities that were not well explored by prior works. As a result, we manage to identify exploitable vulnerabilities on certain ADC registers that are involved in the analog-to-digital conversion logic. As a proof of concept, we construct and develop three attack techniques by exploiting the vulnerabilities identified. Finally, we test the attacks on a mini-CPS testbed we designed using IoT devices, analog sensors and actuators. Our experimental results reveal high effectiveness of the proposed attack techniques in misleading PLCs to make incorrect control decisions in CPS. We also analyze the impact of such attacks when launched in real-word CPS testbeds.</p>

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Section: B the Proposed Attacksmentioning

confidence: 99%

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confidence: 99%

Exploiting Vulnerabilities of ADC Registers in IoT and Control Systems

Chekole,

Thulasiraman,

Zhou

2023

Preprint

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“…Demand specifies the number of goods and services that will be used or consumed in an economy at a given time. Cyber physical system (CPS)-based demand forecasting systems (CDFS) are ubiquitous in CNI [2] where sensor nodes are deployed in the environment to monitor the current demand for the services of CNI (e.g., traffic and electricity consumption) [3][4][5][6][7]. The sensors transmit their measurements at a regular interval to a base station through one or more intermediate nodes depending on the distance between the sensor and the base station.…”

Section: Introductionmentioning

confidence: 99%

A Resilient Cyber-Physical Demand Forecasting System for Critical Infrastructures against Stealthy False Data Injection Attacks

et al. 2022

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The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly vulnerable to being compromised under false data injection attacks (FDIAs). The problem is that the majority of existing CDFS employ anomaly-based intrusion detection systems (AIDS) to combat FDIAs. Since the distribution of demand time series keeps changing naturally with time, AIDS treat a major change in the distribution as an attack, but this approach is not effective against colluding FDIAs. To overcome this problem, we propose a novel resilient CDFS called PRDFS (Proposed Resilient Demand Forecasting System). The primary novelty of PRDFS is that it uses signature-based intrusion detection systems (SIDS) that automatically generate attack signatures through the game-theoretic approach for the early detection of malicious nodes. We simulate the performance of PRDFS under colluding FDIA on High Performance Computing (HPC). The simulation results show that the demand forecasting resilience of PRDFS never goes below 80%, regardless of the percentage of malicious nodes. In contrast, the resilience of the benchmark system decreases sharply from about 99% to less than 30%, over the simulation period as the percentage of malicious nodes increases.

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EARIC: Exploiting ADC Registers in IoT and Control Systems

Chekole,

Thulasiraman,

Zhou

2023

Lecture Notes in Computer Science

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An analog-to-digital converter (ADC) is a critical part of most computing systems that converts analog signals into quantifiable digital values. Since most digital devices operate only on digital values, the ADC acts as an interface between the digital and analog worlds. As such, ADCs are commonly used in a widerange of applications, including internet of things (IoT), industrial control systems (ICS), cyber-physical systems (CPS), audio/video devices, medical imaging, digital oscilloscopes, and cell phones, among others. For example, programmable logic controllers (PLCs) in ICS/CPS often make control decisions based on digital values converted from analog signals by ADCs. Due to its crucial role in various applications, ADCs are often targeted by a widerange of physical and cyber attacks. Attackers often exploit vulnerabilities that could be found in the software/hardware of ADCs. In this work, we first conduct a deeper study in the ADC conversion logic to investigate relevant vulnerabilities that were not well explored by prior works. As a result, we manage to identify exploitable vulnerabilities on certain ADC registers that are involved in the analog-to-digital conversion logic. As a proof of concept, we construct and develop three attack techniques by exploiting the vulnerabilities identified. Finally, we test the attacks on a mini-CPS testbed we designed using IoT devices, analog sensors and actuators. Our experimental results reveal high effectiveness of the proposed attack techniques in misleading PLCs to make incorrect control decisions in CPS. We also analyze the impact of such attacks when launched in real-word CPS testbeds.

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SCOPE: Secure Compiling of PLCs in Cyber-Physical Systems

Cited by 7 publications

References 17 publications

Exploiting Vulnerabilities of ADC Registers in IoT and Control Systems

Exploiting Vulnerabilities of ADC Registers in IoT and Control Systems

A Resilient Cyber-Physical Demand Forecasting System for Critical Infrastructures against Stealthy False Data Injection Attacks

EARIC: Exploiting ADC Registers in IoT and Control Systems

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