Security Perspective Analysis of Industrial Cyber Physical Systems (I-CPS): A Decade-wide Survey

Agrawal, Neha; Kumar, Rohit

doi:10.1016/j.isatra.2022.03.018

Cited by 28 publications

(6 citation statements)

References 87 publications

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“…As a result, fundamental cybersecurity practices such as implementing complex passwords or enforcing stringent authentication requirements were perceived as hindrances to system accessibility. Consequently, designers and operators of these systems deliberately avoided such measures [7]. Similarly, traditional anti-malware programs like signature-based antivirus tools were eschewed to mitigate the risk of false positives mistakenly quarantining or disabling crucial system operations.…”

Section: Background and Rationalementioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges.

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Section: Background and Rationalementioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

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“…In addition to the machine learning techniques, deep learning models, and advanced algorithms mentioned earlier, there are other tools and approaches that can be explored in the context of insider IDS in SCADA systems (Table IV  Data Fusion: To increase the precision of insider threat detection, data fusion merges data from many sources, including sensor data, network logs, and user behaviour. Complex patterns and correlations that might not be obvious when evaluating individual data sources might be found by integrating various data streams [20].…”

Section: B Deep Learning Modelsmentioning

confidence: 99%

“…The emphasis on industrial protocols and network segmentation in SCADA can help CPS, and SCADA can gain knowledge from CPS's cutting-edge encryption and authentication methods for securing data flow in integrated systems. Additionally, SCADA can benefit from CPS's distributed control architecture and adaptive communication protocols for increased resiliency and flexibility in a variety of industrial settings, while CPS can learn from SCADA's data filtering strategies and emphasis on redundancy for improved efficiency and continuous real-time control [20], [21].…”

Section: Comparison Of Ids Scada and Cyber Physical Systems (Table Iii)mentioning

confidence: 99%

Cybersecurity Advances in SCADA Systems

Al-Muntaser,

Mohamed,

Tuama

et al. 2023

IJACSA

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The management of critical infrastructure heavily relies on Supervisory Control and Data Acquisition [SCADA] systems, but as they become more connected, insider attacks become a greater concern. Insider threat detection systems [IDS] powered by machine learning have emerged as a potential answer to this problem. In order to identify and neutralize insider threats, this review paper examines the most recent developments in machine learning algorithms for insider IDS in SCADA security systems. A thorough analysis of research articles published in 2019 and later, focussed on variety of machine learning methods, have been adopted in this review study to better highlight difficulties and challenges being faced by professionals, and how the study will contribute to overcome them. The results show that, in addition to conventional methods, machine-learning based intrusion detection techniques offer important advantages in identifying complex and covert insider attacks. Finding pertinent insider threat data for model training and guaranteeing data privacy and security are still difficult to address. Ensemble techniques and hybrid strategies show potential for improving detection resiliency. In conclusion, machine learning-based insider IDS has the potential to protect critical infrastructures by strengthening SCADA systems against insider attacks. The similarities and differences between cyber physical systems and SCADA systems, emphasizing security challenges and the potential for mutual improvement were also reviewed in this study. In order to be as effective as possible, future research should concentrate on addressing issues with data collecting and privacy, investigating the latest developments in technology, and creating hybrid models. SCADA systems can accomplish proactive and effective defence against insider attacks by integrating machine learning advancements, maintaining their dependability and security in the face of emerging threats.

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“…Security threats may also focus on the availability of industrial systems, to make them unable to do their typical tasks through overloading [63]. Different types of physical and cyber-attacks can threaten the availability of an IIoT system, such as DoS attacks, DDoS attacks, Mirai botnet, BrickerBot, and Reaper.…”

Section: ) Industrial Iot (Iiot)mentioning

confidence: 99%

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

et al. 2022

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Communication systems and networks are evolving as an integral part of not only of our everyday life but also as a part of the industry, fundamental infrastructures, companies, etc. Current directions and concepts, such as the Internet of Things (IoT), promise enhanced quality of life, greater business opportunities, cost-effective manufacturing, and efficient operation management through ubiquitous connectivity and deployment of smart physical objects. IoT networks are able to collect, preprocess, and transmit a huge amount of data. A considerable portion of this data is security-and privacycritical data, which makes IoT networks a tempting option for attackers. Given that these networks deal with the actual aspects of our lives and fundamental infrastructures (e.g. smart grids), cybersecurity in such networks is crucial. The large scale of these networks and their unique characteristics and complexity bring further vulnerabilities. In this study we give an introduction to IoT, focusing on the IoT application layer, security requirements, threats, and countermeasures in this layer, and some of the open issues and future research lines.

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Security Perspective Analysis of Industrial Cyber Physical Systems (I-CPS): A Decade-wide Survey

Cited by 28 publications

References 87 publications

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Cybersecurity Advances in SCADA Systems

Security in the Internet of Things Application Layer: Requirements, Threats, and Solutions

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