Cybersecurity in process control, operations, and supply chain

Parker, Sandra; Wu, Zhe; Christofides, Panagiotis D.

doi:10.1016/j.compchemeng.2023.108169

Cited by 24 publications

(4 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiplicative FDI attacks alter the stability properties of a closed-loop process by modifying the eigenvalues of the matrix A ξ a (K, L) in Equation (9). As a result, the detectability of such attacks may be influenced by the stability of the closed-loop process under an attack.…”

Section: Control Mode Switching For Cyberattack Detectionmentioning

confidence: 99%

“…Due to the interconnected nature of a cyber-physical system like a PCS, a comprehensive solution to enhancing the cybersecurity of PCSs may involve adopting a multi-faceted approach that involves both information technology-based and operational technologybased approaches. The approaches proposed in the literature broadly include those that reinforce information technology-based infrastructure (e.g., firewalls [3] and network design-based approaches [4,5]), approaches that involve cybersecure architecture design (e.g., [6]), approaches for process equipment design to mitigate adverse impacts of a cyberattack (e.g., [7,8]), and the design of operational technology (OT)-based approaches for detecting, identifying, and successfully recovering from an ongoing cyberattack [6,[9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Multiplicative False Data Injection Cyberattacks on Process Control Systems via Randomized Control Mode Switching

Narasimhan,

Ellis,

El-Farra

2024

Processes

View full text Add to dashboard Cite

A fundamental problem at the intersection of process control and operations is the design of detection schemes monitoring a process for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify operational data with a multiplicative factor and could be designed to be detection evading without in-depth process knowledge. In a prior work, we presented a control mode switching strategy that enhances the detection of multiplicative FDI attacks in processes operating at steady state (when process states evolve within a small neighborhood of the steady state). Control mode switching on the attack-free process at steady-state may induce transients and generate false alarms in the detection scheme. To minimize false alarms, we subsequently developed a control mode switch-scheduling condition for processes with an invertible output matrix. In the current work, we utilize a reachable set-based detection scheme and use randomized control mode switches to augment attack detection capabilities. The detection scheme eliminates potential false alarms occurring from control mode switching, even for processes with a non-invertible output matrix, while the randomized switching helps bolster the confidentiality of the switching schedule, preventing the design of a detection-evading “smart” attack. We present two simulation examples to illustrate attack detection without false alarms, and the merits of randomized switching (compared with scheduled switching) for the detection of a smart attack.

show abstract

Section: Control Mode Switching For Cyberattack Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Multiplicative False Data Injection Cyberattacks on Process Control Systems via Randomized Control Mode Switching

Narasimhan,

Ellis,

El-Farra

2024

Processes

View full text Add to dashboard Cite

show abstract

“…2) Using reliable model supply channels: By choosing reputable and trustworthy MLaaS providers, organizations can significantly reduce the likelihood of acquiring malware-infected models [30].…”

Section: Countermeasuresmentioning

confidence: 99%

Attacks on Machine Learning Models Based on the PyTorch Framework

Namiot,

Bidzhiev

2024

ARC

View full text Add to dashboard Cite

This research delves into the cybersecurity implications of neural network training in cloud-based services. Despite their recognition for solving IT problems, the resource-intensive nature of neural network training poses challenges, leading to increased reliance on cloud services. However, this dependence introduces new cybersecurity risks. The study focuses on a novel attack method exploiting neural network weights to discreetly distribute hidden malware. It explores seven embedding methods and four trigger types for malware activation. Additionally, the paper introduces an open-source framework automating code injection into neural network weight parameters, allowing researchers to investigate and counteract this emerging attack vector.

show abstract

“…The results of the study are of value for the protection and construction of cybersecurity at the present time of digital transformation [14]. Parker S. et al briefly analyzed the forms of intelligent cyberattacks in industrial control systems and proposed machine learning-based detection methods and related strategies aimed at achieving secure communications for process control, process operations, and supply chain management [15]. O'Brien, P. studied and analyzed cybersecurity on the factory floor and pointed out that automated cybersecurity is also an important part of cybersecurity.…”

Section: Introductionmentioning

confidence: 99%

Optimization of network security protection posture based on data clustering

Zhu

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

This paper focuses on the challenges facing network security in the digital era and proposes a network security posture optimization method based on data clustering. Three mainstream network security models, namely, P2DR security operation and maintenance model, line defense model, and three-dimensional defense model, are analyzed, and the limitations of existing security products are pointed out. The application potential of big data technology in network security is emphasized, and a comprehensive technical process containing information extraction, posture modeling, security trend prediction, and security policy deployment is constructed. The ARMA model and reinforcement learning building model are introduced, and the improved K-means algorithm is proposed to address the shortcomings of traditional methods. Experiments are conducted using the DARPA2000 dataset, and the results show the enhanced algorithm’s significant improvement in clustering accuracy and stability, with a maximum threat value of about 160, demonstrating better stability and effectiveness than the traditional method. The posture value exceeds 500 in a specific period, highlighting the dynamic changes in network security and confirming the practicality and effectiveness of the technique. The results of this study provide new strategies and perspectives for network security protection, and have essential reference and guidance value for practical applications and future research.

show abstract

Cybersecurity in process control, operations, and supply chain

Cited by 24 publications

References 92 publications

Detection of Multiplicative False Data Injection Cyberattacks on Process Control Systems via Randomized Control Mode Switching

Detection of Multiplicative False Data Injection Cyberattacks on Process Control Systems via Randomized Control Mode Switching

Attacks on Machine Learning Models Based on the PyTorch Framework

Optimization of network security protection posture based on data clustering

Contact Info

Product

Resources

About