A new safety and security risk analysis framework for industrial control systems

Kriaa, Siwar; Bouissou, Marc; Laarouchi, Youssef

doi:10.1177/1748006x18765885

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…In her doctoral thesis, Kriaa (2016) described the SCADA safety and security joint modelling method (S-cube). Building on previous work using a Boolean logic Driven Markov Processes (BDMP) formalism in industrial security scenarios (Kriaa et al, 2012), the S-cube method includes a taxonomy of attack vectors and a knowledge base of failure/ attack steps and propagations (Kriaa et al, 2019). This knowledge base in S-cube addresses the dependency on expert knowledge mentioned for STPA-Sec and CHASSIS.…”

Section: Existing Methods For Integrated Safety and Security Analysis...mentioning

confidence: 99%

“…Furthermore, S-cube offers a better scalability of the analysis that adapts to different levels of abstraction of the system (Kriaa et al, 2013). However, S-cube decouples the information flows in the digital control system from the analysis of energies controlled in the physical plant (Kriaa et al, 2019). This decoupling, aimed at keeping the analysis scope into the domain of the digital control system, may be a critical weakness to identify new and unknown risks that have important safety repercussions in the physical domain of the system.…”

Section: Existing Methods For Integrated Safety and Security Analysis...mentioning

confidence: 99%

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An integrated safety and security analysis for cyber-physical harm scenarios

2021

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Section: Existing Methods For Integrated Safety and Security Analysis...mentioning

confidence: 99%

Section: Existing Methods For Integrated Safety and Security Analysis...mentioning

confidence: 99%

An integrated safety and security analysis for cyber-physical harm scenarios

2021

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“…For instance, cyber attacks against IoT systems can cause negative consequences in the physical world and, thus, compromise the safety of the user and the environment. In this regard, several studies have attested that, despite the differences and dichotomies between the safety and security properties, they still share several commonalities, interdependence, and relationships, which make them closely intertwined for a viable analysis of many systems, including the IoT [18][19][20]23,[58][59][60].…”

Section: Relationship Between Iot Safety and Security Analysismentioning

confidence: 99%

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

et al. 2023

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The rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework.

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“…Emergencies arise due to causes such as explosions in a processing plant, accidents in storage facilities, during transportation, or due to technological failures, inappropriate or inadequate plant safety design, arson, malicious acts, human error, and natural calamities. Therefore, it is essential to investigate petroleum refineries' worst possible safety hazards, evaluate their impact on people, property, plants, and society, and devise risk-control strategies [15].…”

Section: Introductionmentioning

confidence: 99%

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

Gabhane

Rao

2023

Toxics

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Risk analysis and prediction is a primary monitoring strategy to identify abnormal events occurring in chemical processes. The accidental release of toxic gases may result in severe problems for people and the environment. Risk analysis of hazardous chemicals using consequence modeling is essential to improve the process reliability and safety of the refineries. In petroleum refineries: toluene, hydrogen, isooctane, kerosene, methanol, and naphtha are key process plants with toxic and flammable chemicals. The major process plants considered for risk assessment in the refinery are the gasoline hydrotreatment unit, crude distillation, aromatic recovery, continuous catalytic reformer, methyl–tert–butyl–ether, and kerosene merox units. Additionally, we propose a threat and risk analysis neural network for the chemical explosion (TRANCE) model for refinery incident scenarios. Significantly, 160 attributes were collected for the modeling on the basis of the significance of failure and hazardous chemical leaks in the refinery. Hazard analysis shows that the leakages of hydrogen and gasoline at the gasoline hydrotreatment unit, kerosene at the kerosene merox plant, and crude oil at crude-distillation units were areas of profound concern. The developed TRANCE model predicted the chemical explosion distance with an R2 accuracy value of 0.9994 and MSE of 679.5343.

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A new safety and security risk analysis framework for industrial control systems

Cited by 8 publications

References 24 publications

An integrated safety and security analysis for cyber-physical harm scenarios

An integrated safety and security analysis for cyber-physical harm scenarios

An Overview of Safety and Security Analysis Frameworks for the Internet of Things

Threat and Risk Analysis-Based Neural Network for a Chemical Explosion (TRANCE) Model to Predict Hazards in Petroleum Refinery

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