MSICST: Multiple-Scenario Industrial Control System Testbed for Security Research

Xu, Wei; Tao, Yaodong; Yang, Ching-Nung; Chen, Huiqin

doi:10.32604/cmc.2019.05678

Cited by 10 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MSICST (Multiple-Scenario Industrial Control System Testbed) [80] is a hybrid representation of four different ICS scenarios: a thermal power plant, a rail transit, a smart grid, and intelligent manufacturing. Physical processes are always simulated while the control systems are built using commercial hardware and software.…”

Section: F Hybridmentioning

confidence: 99%

A Survey on Industrial Control System Testbeds and Datasets for Security Research

Conti,

Donadel,

Turrin

2021

Preprint

View full text Add to dashboard Cite

The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs) open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore, since ICSs are often employed in critical infrastructures (e.g., nuclear plants) and manufacturing companies (e.g., chemical industries), attacks can lead to devastating physical damages. In dealing with this security requirement, the research community focuses on developing new security mechanisms such as Intrusion Detection Systems (IDSs), facilitated by leveraging modern machine learning techniques. However, these algorithms require a testing platform and a considerable amount of data to be trained and tested accurately. To satisfy this prerequisite, Academia, Industry, and Government are increasingly proposing testbed (i.e., scaled-down versions of ICSs or simulations) to test the performances of the IDSs. Furthermore, to enable researchers to cross-validate security systems (e.g., security-bydesign concepts or anomaly detectors), several datasets have been collected from testbeds and shared with the community.In this paper, we provide a deep and comprehensive overview of ICSs, presenting the architecture design, the employed devices, and the security protocols implemented. We then collect, compare, and describe testbeds and datasets in the literature, highlighting key challenges and design guidelines to keep in mind in the design phases. Furthermore, we enrich our work by reporting the best performing IDS algorithms tested on every dataset to create a baseline in state of the art for this field. Finally, driven by knowledge accumulated during this survey's development, we report advice and good practices on the development, the choice, and the utilization of testbeds, datasets, and IDSs.

show abstract

Section: F Hybridmentioning

confidence: 99%

A Survey on Industrial Control System Testbeds and Datasets for Security Research

Conti,

Donadel,

Turrin

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The main goal of this work is to identify impacts of Industry 4.0 on Supply Chain Management (SCM) as well as logistics in order to support these business units in digitalization issues . Additionally, several projects introduce applications supporting the engineering process within industrial systems by applying decision trees or security frameworks [26][27][28].…”

Section: Model-based Systems Engineeringmentioning

confidence: 99%

Towards a Domain-Specific Approach Enabling Tool-Supported Model-Based Systems Engineering of Complex Industrial Internet-of-Things Applications

Binder

Neureiter

Lüder

2021

Systems

View full text Add to dashboard Cite

Contemporary manufacturing systems are undergoing a major change promoted by emerging technologies such as Cyber-physical Systems (CPS) or the Internet of Things (IoT). This trend, nowadays widely known by the term “Industry 4.0”, leads to a new kind of automated production. However, the rising number of dynamically interconnected elements in industrial production lines results in such a system being transformed into a complex System of Systems (SoS). Due to the increasing complexity and the challenges accompanied by this change, conventional engineering methods using generic principles reach their limits when developing this type of systems. With varying approaches only trying to find a solution for small-scaled areas of this problem statement, the need for a holistic methodology becomes more and more obvious. Having recognized this issue, one of the most promising approaches has been introduced with the Reference Architecture Model Industry 4.0 (RAMI 4.0). However, in the current point of view, this domain-specific architecture framework is missing specifications to address all aspects of such a critical infrastructure. Thus, this paper introduces a comprehensive modeling approach utilizing methods applied in Model-Based Systems Engineering (MBSE) and including domain-specific particularities as well as architectural concepts with the goal to enable mutual engineering of current and future industrial systems. The resulting artifacts, a domain-specific language (DSL), an architecture definition and a development process, are thereby consolidated in a ready to use software framework, whose applicability was evaluated by a real-world case study.

show abstract

“…At the point of publication of this research, there is only one research article related to application of ICS cyber kill chain that consists of multiplescenario ICS testbed for thermal power plant, rail transit, smart grid, and intelligent manufacturing with two typical attack scenarios [38]. Although modified versions of cyber kill chain model have been applied in other domains like multimedia service environments [39], Internet-of-Things (IoT) systems [40], security information and event management (SIEM) software [41], and cyber-physical system [42].…”

Section: State Of the Art Of Currently Used Technologies In Railwaymentioning

confidence: 99%

Railway Defender Kill Chain to Predict and Detect Cyber-Attacks

Kour

Thaduri

Karim

2020

JCSANDM

View full text Add to dashboard Cite

Most organizations focus on intrusion prevention technologies, with less emphasis on prediction and detection. This research looks at prediction and detection in the railway industry. It uses an extended cyber kill chain (CKC) model and an industrial control system (ICS) cyber kill chain for detection and proposes predictive technologies that will help railway organizations predict and recover from cyber-attacks. The extended CKC model consists of both internal and external cyber kill chain; breaking the chain at an early stage will help the defender stop the adversary’s malicious actions. This research incorporates an OSA (open system architecture) for railways with the railway cybersecurity OSA-CBM (open system architecture for condition-based maintenance) architecture. The railway cybersecurity OSACBM architecture consists of eight layers; cybersecurity information moves from the initial level of data acquisition to data processing, data analysis, incident detection, incident assessment, incident prognostics, decision support, and visualization. The main objective of the research is to predict, prevent, detect, and respond to cyber-attacks early in the CKC by using defensive controls called the Railway Defender Kill Chain (RDKC). The contributions of the research are as follows. First, it adapts and modifies the railway cybersecurity OSA-CBM architecture for railways. Second, it adapts the cyber kill chain model for the railway. Third, it introduces the Railway Defender Kill Chain. Fourth, it presents examples of cyber-attack scenarios in the railway system.

show abstract

MSICST: Multiple-Scenario Industrial Control System Testbed for Security Research

Cited by 10 publications

References 15 publications

A Survey on Industrial Control System Testbeds and Datasets for Security Research

A Survey on Industrial Control System Testbeds and Datasets for Security Research

Towards a Domain-Specific Approach Enabling Tool-Supported Model-Based Systems Engineering of Complex Industrial Internet-of-Things Applications

Railway Defender Kill Chain to Predict and Detect Cyber-Attacks

Contact Info

Product

Resources

About