The Study on the Cyber Security Requirements of Cyber-Physical Systems for Cyber Security Frameworks

The Internet of Things (IoT) and cloud technologies are increasingly implemented in the form of Cyber-Physical Systems of Systems (CPSoS) for the railway sector. In order to satisfy the security requirements of Cyber-Physical Systems (CPS), domainspecific risk identification assessment procedures have been developed. Threat modelling is one of the most commonly used methods for threat identification for the security analysis of CPSoS and is capable of targeting various domains. This paper reports our experience of using a risk management framework identify the most critical security vulnerabilities in CPSoS in the domain and shows the broader impact this work can have on the domain of safety and security management. Moreover, we emphasize the application of common analytical methods for cyber-security based on international industry standards to identify the most vulnerable assets. These will be applied to a meta-model for automated railway systems in the concept phase to support the development and deployment of these systems. Furthermore, it is the first step to create a secure and standard complaint system by design.

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A Preliminary Design-Phase Security Methodology for Cyber–Physical Systems

Systems ◽

10.3390/systems7020021 ◽

2019 ◽

Vol 7 (2) ◽

pp. 21 ◽

Cited By ~ 5

Author(s):

Bryan Carter ◽

Stephen Adams ◽

Georgios Bakirtzis ◽

Tim Sherburne ◽

Peter Beling ◽

...

Keyword(s):

Systems Engineering ◽

Cyber Security ◽

Cyber Physical Systems ◽

Security Requirements ◽

Complex Nature ◽

Theoretic Model ◽

Physical Systems ◽

Model Based ◽

The Face ◽

Comparison Of The Results

Despite “cyber” being in the name, cyber–physical systems possess unique characteristics that limit the applicability and suitability of traditional cybersecurity techniques and strategies. Furthermore, vulnerabilities to cyber–physical systems can have significant safety implications. The physical and cyber interactions inherent in these systems require that cyber vulnerabilities not only be defended against or prevented, but that the system also be resilient in the face of successful attacks. Given the complex nature of cyber–physical systems, the identification and evaluation of appropriate defense and resiliency strategies must be handled in a targeted and systematic manner. Specifically, what resiliency strategies are appropriate for a given system, where, and which should be implemented given time and/or budget constraints? This paper presents two methodologies: (1) the cyber security requirements methodology and (2) a systems-theoretic, model-based methodology for identifying and prioritizing appropriate resiliency strategies for implementation in a given system and mission. This methodology is demonstrated using a case study based on a hypothetical weapon system. An assessment and comparison of the results from the two methodologies suggest that the techniques presented in this paper can augment and enhance existing systems engineering approaches with model-based evidence.

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Managing Cyber Security Risks of the Cyber-Enabled Ship

Journal of Marine Science and Engineering ◽

10.3390/jmse8100768 ◽

2020 ◽

Vol 8 (10) ◽

pp. 768

Author(s):

Georgios Kavallieratos ◽

Sokratis Katsikas

Keyword(s):

Cyber Security ◽

Cyber Physical Systems ◽

Transformation Process ◽

Cyber Attacks ◽

Security Requirements ◽

Shipping Industry ◽

Security Risks ◽

Industrial Control Systems ◽

Physical Systems ◽

Cyber Risk

One aspect of the digital transformation process in the shipping industry, a process often referred to as Shipping 4.0, is the increased digitization of on board systems that goes along with increased automation in and autonomy of the vessel. This is happening by integrating Information Technology with Operation Technology systems that results in Cyber Physical Systems on which the safe operations and sailing of contemporary and future vessels depend. Unavoidably, such highly interconnected and interdependent systems increase the exposure of the vessel’s digital infrastructure to cyber attacks and cyber security risks. In this paper, we leverage the STRIDE and DREAD methodologies to qualitatively and quantitatively assess the cyber risk of Cyber Physical Systems on board digitalized contemporary and future ships. Further, we propose appropriate cyber security baseline controls to mitigate such risks, by applying a systematic approach using a set of criteria that take into account the security requirements; the cyber risks; the possible attacks; and the possibly already existing controls, to select from the list of controls provided in the Industrial Control Systems (ICS) overlay of the NIST Guide to ICS Security. The results are expected to support the decision-making and the design of a security architecture for the cyber-enabled ship.

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Security of Cyber-Physical Systems: A Generalized Algorithm for Intrusion Detection and Determining Security Robustness of Cyber Physical Systems using Logical Truth Tables

Vanderbilt Undergraduate Research Journal ◽

10.15695/vurj.v9i0.3765 ◽

2013 ◽

Vol 9 ◽

Cited By ~ 4

Author(s):

Curtis G. Northcutt

Keyword(s):

Intrusion Detection ◽

Cyber Security ◽

Logical Truth ◽

Cyber Physical Systems ◽

Security Model ◽

Security Measures ◽

Physical Systems ◽

Multiple Signal ◽

Security Attack ◽

Truth Tables

The recent proliferation of embedded cyber components in modern physical systems [1] has generated a variety of new security risks which threaten not only cyberspace, but our physical environment as well. Whereas earlier security threats resided primarily in cyberspace, the increasing marriage of digital technology with mechanical systems in cyber-physical systems (CPS), suggests the need for more advanced generalized CPS security measures. To address this problem, in this paper we consider the first step toward an improved security model: detecting the security attack. Using logical truth tables, we have developed a generalized algorithm for intrusion detection in CPS for systems which can be defined over discrete set of valued states. Additionally, a robustness algorithm is given which determines the level of security of a discrete-valued CPS against varying combinations of multiple signal alterations. These algorithms, when coupled with encryption keys which disallow multiple signal alteration, provide for a generalized security methodology for both cyber-security and cyber-physical systems.

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A Blockchain-based Deep Learning Approach for Cyber Security in Next Generation Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2020.3040968 ◽

2020 ◽

pp. 1-1

Author(s):

Shailendra Rathore ◽

J. H. Park

Keyword(s):

Deep Learning ◽

Cyber Security ◽

Cyber Physical Systems ◽

Learning Approach ◽

Next Generation ◽

Physical Systems

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Making (Implicit) Security Requirements Explicit for Cyber-Physical Systems: A Maritime Use Case Security Analysis

Communications in Computer and Information Science - Database and Expert Systems Applications ◽

10.1007/978-3-030-27684-3_11 ◽

2019 ◽

pp. 75-84 ◽

Cited By ~ 1

Author(s):

Tope Omitola ◽

Abdolbaghi Rezazadeh ◽

Michael Butler

Keyword(s):

Security Analysis ◽

Cyber Physical Systems ◽

Security Requirements ◽

Use Case ◽

Physical Systems

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Cyber security in cyber-physical systems: on false data injection attacks in the smart grid

Cyber-Physical system design with sensor networking technologies ◽

10.1049/pbce096e_ch5 ◽

2016 ◽

pp. 105-126

Author(s):

Jie Lin ◽

Wei Yu ◽

Guobin Xu ◽

Xinyu Yang

Keyword(s):

Smart Grid ◽

Cyber Security ◽

Cyber Physical Systems ◽

Physical Systems ◽

False Data Injection ◽

Injection Attacks

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Adoption of Machine Learning With Adaptive Approach for Securing CPS

Handbook of Research on Machine and Deep Learning Applications for Cyber Security - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-9611-0.ch018 ◽

2020 ◽

pp. 388-415

Author(s):

Rama Mercy Sam Sigamani

Keyword(s):

Machine Learning ◽

Cyber Security ◽

Smart Grids ◽

False Negative ◽

False Negative Rate ◽

Cyber Physical Systems ◽

Detection Accuracy ◽

Physical Systems ◽

Positive Rate ◽

Iot Devices

The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence algorithms have been proposed to detect and mitigate the cyber-attacks in cyber physical systems, smart grids, power systems. The various machine learning approaches towards securing CPS is observed based on the performance metrics like detection accuracy, average classification rate, false negative rate, false positive rate, processing time per packet. A unique feature of CPS is considered through structural adaptation which facilitates a self-healing CPS.

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