Risk Management for CPS Security

Ali, Saqib; Balushi, Taiseera Al; Nadir, Zia; Hussain, Omar Khadeer

doi:10.1007/978-3-319-75880-0_2

Cited by 10 publications

(8 citation statements)

References 34 publications

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“…Several security risk assessment methods applicable to general purpose IT systems have appeared in the literature (see Reference [ 8 ] for a comprehensive survey). Even though several of these methods can be and have been applied to CPSs, they cannot accurately assess cyber risks related to CPSs according to Reference [ 9 ], where a number of approaches for risk assessment for CPSs are listed. A review of risk assessment methods for CPSs, from the perspective of safety, security, and their integration, including a proposal for some classification criteria was made in Reference [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

Kavallieratos

Σπαθούλας

Katsikas

2021

Sensors

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The increasingly witnessed integration of information technology with operational technology leads to the formation of Cyber-Physical Systems (CPSs) that intertwine physical and cyber components and connect to each other to form systems-of-systems. This interconnection enables the offering of functionality beyond the combined offering of each individual component, but at the same time increases the cyber risk of the overall system, as such risk propagates between and aggregates at component systems. The complexity of the resulting systems-of-systems in many cases leads to difficulty in analyzing cyber risk. Additionally, the selection of cybersecurity controls that will effectively and efficiently treat the cyber risk is commonly performed manually, or at best with limited automated decision support. In this work, we propose a method for analyzing risk propagation and aggregation in complex CPSs utilizing the results of risk assessments of their individual constituents. Additionally, we propose a method employing evolutionary programming for automating the selection of an optimal set of cybersecurity controls out of a list of available controls, that will minimize the residual risk and the cost associated with the implementation of these measures. We illustrate the workings of the proposed methods by applying them to the navigational systems of two variants of the Cyber-Enabled Ship (C-ES), namely the autonomous ship and the remotely controlled ship. The results are sets of cybersecurity controls applied to those components of the overall system that have been identified in previous studies as the most vulnerable ones; such controls minimize the residual risk, while also minimizing the cost of implementation.

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Section: Related Workmentioning

confidence: 99%

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

Kavallieratos

Σπαθούλας

Katsikas

2021

Sensors

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“…In other words, if the capabilities of ESs increase in terms of points of connection and input units, then attack surfaces increase, thereby increasing the probability that the system is hacked. [26] ES design principles [20] ESs' security from new dimension [27] Security-aware and real-time ES [28] Hardware enhancement of ES [29] Cybersecurity for cyber-physical system [22] IoT and security measurements [30] Taxonomy of detection approaches [31] Data privacy and policy problems survey [13] Security requirements and challenges in IoT [32] Challenges, threats and solutions of IoT [33] A roadmap for security challenges in the IoT [7] A taxonomy of threats, vulnerabilities and attacks. [34] A survey of cyber-physical systems security [35] Security framework for IoT…”

Section: A Security Problemsmentioning

confidence: 99%

“…This threat can be an intentional action, accidental event, or an abnormal circumstance. Cyber threats include unstructured threats, which use existing hacking tools, and structured threats, e.g., Advanced Persistent Threats (APTs), conducted by an expert attacker [25], [29], [46], [47]. APT is a prolonged and targeted cyberattack in which an intruder gains access to a network and tries to remain undetected.…”

Section: B Classic Attacks and Impact On Essmentioning

confidence: 99%

“…For network-connected systems, considering the four layers of Transmission Control Protocol/Internet Protocol (TCP/IP) in early stages of designing the embedded system will contribute to hardening the ES. The layers of TCP/IP are Application layer, Transport layer, Network layer, and Link and Physical layer [55]; these layers play an important role in terms of the security of the embedded system, as the weakness of the measures taken in these layers will create many vulnerabilities that the attacker can exploit, and in return, the implementing of best practices in these layers will enhance the stability of the system. The biggest challenge facing an embedded system is when it is connected to the public Internet, as it faces unpredictable cyber threats.…”

Section: B Classic Attacks and Impact On Essmentioning

confidence: 99%

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Analytical Review of Cybersecurity for Embedded Systems

Aloseel

Shaw

et al. 2021

IEEE Access

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To identify the key factors and create the landscape of cybersecurity for embedded systems (CSES), an analytical review of the existing research on CSES has been conducted. The common properties of embedded systems, such as mobility, small size, low cost, independence, and limited power consumption when compared to traditional computer systems, have caused many challenges in CSES. The conflict between cybersecurity requirements and the computing capabilities of embedded systems makes it critical to implement sophisticated security countermeasures against cyber-attacks in an embedded system with limited resources, without draining those resources. In this study, twelve factors influencing CSES have been identified: ( 1) the components; (2) the characteristics; (3) the implementation; (4) the technical domain; (5) the security requirements; (6) the security problems; (7) the connectivity protocols; (8) the attack surfaces; (9) the impact of the cyber-attacks; (10) the security challenges of the ESs; (11) the security solutions; and(12) the players (manufacturers, legislators, operators, and users). A Multiple Layers Feedback Framework of Embedded System Cybersecurity (MuLFESC) with nine layers of protection is proposed, with new metrics of risk assessment. This will enable cybersecurity practitioners to conduct an assessment of their systems with regard to twelve identified cybersecurity aspects. In MuLFESC, the feedback from the systemcomponents layer to the system-operations layer could help implement "Security by Design" in the design stage at the bottom layer. The study provides a clear landscape of CSES and, therefore, could help to find better comprehensive solutions for CSES.INDEX TERMS Characteristics of embedded system, countermeasures, embedded system, cybersecurity of embedded system, MuLFESC, risk assessment.

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“…The growth of cloud computing and IoT have brought their own set of challenges in the form of increased attack surfaces and data security. Current cyber security solutions are not capable of stopping these threats in terms of their efficacy and scalability [17], [21]. In addition, there is a trend in increasing attack sophistication and speed of attack propagation as the internet has reached a global scale, making it possible to launch sophisticated attacks at little or no development costs in a few seconds to target entities across the globe [20].…”

Section: Anomaly Behavior Analysismentioning

confidence: 99%

Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes

et al. 2020

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The Internet of Things (IoT) represents a mean to share resources (memory, storage computational power, data, etc.) between computers and mobile devices, as well as buildings, wearable devices, electrical grids, and automobiles, just to name few. The IoT is leading to the development of advanced information services that will require large storage and computational power, as well as real-time processing capabilities. The integration of IoT with emerging technologies such as Fog Computing can complement these requirements with pervasive and cost-effective services capable of processing largescale geo-distributed information. In any IoT application, communication availability is essential to deliver accurate and useful information, for instance, to take actions during dangerous situations, or to manage critical infrastructures. IoT components like gateways, also called Fog Nodes, face outstanding security challenges as the attack surface grows with the number of connected devices requesting communication services. These Fog nodes can be targeted by an attacker, preventing the nodes from delivering important information to the final users or to perform accurate automated actions. This paper introduces an Anomaly Behavior Analysis Methodology based on Artificial Neural Networks, to implement an adaptive Intrusion Detection System (IDS) capable of detecting when a Fog node has been compromised, and then take the required actions to ensure communication availability. The experimental results reveal that the proposed approach has the capability for characterizing the normal behavior of Fog Nodes despite its complexity due to the adaptive scheme, and also has the capability of detecting anomalies due to any kind of sources such as misuses, cyber-attacks or system glitches, with high detection rate and low false alarms. INDEX TERMS Anomaly behavior, cyber security, fog computing, IoT, neural networks.

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Risk Management for CPS Security

Cited by 10 publications

References 34 publications

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

Cyber Risk Propagation and Optimal Selection of Cybersecurity Controls for Complex Cyberphysical Systems

Analytical Review of Cybersecurity for Embedded Systems

Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes

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