scholarly journals Cyber Risks to Critical Smart Grid Assets of Industrial Control Systems

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5501
Author(s):  
Chenyang Liu ◽  
Yazeed Alrowaili ◽  
Neetesh Saxena ◽  
Charalambos Konstantinou
Keyword(s):  
Smart Grid ◽  
Control Systems ◽  
Transmission Lines ◽  
Power Supply ◽  
Electric Grid ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
The Past ◽  
Failure To Protect ◽  
The Cost

Cybersecurity threats targeting industrial control systems (ICS) have significantly increased in the past years. Moreover, the need for users/operators to understand the consequences of attacks targeting these systems and protect all assets is vital. This work explores asset discovery in ICS and how to rank these assets based on their criticality. This paper also discusses asset discovery and its components. We further present existing solutions and tools for asset discovery. We implement a method to identify critical assets based on their connection and discuss related results and evaluation. The evaluation utilises four attack scenarios to stress the importance of protecting these critical assets since the failure to protect them can lead to serious consequences. Using a 12-bus system case, our results show that targeting such a system can increase and overload transmission lines values to 120% and 181% MVA, which can affect the power supply and disrupt service, and it can increase the cost up to 60%, affecting the productivity of this electric grid.

Cybersecurity Leadership: Competencies, Governance, and Technologies for Industrial Control Systems

2017 ◽  
Vol 17 (01) ◽  
pp. 1740001 ◽  
Author(s):  
JEAN-PIERRE AUFFRET ◽  
JANE L. SNOWDON ◽  
ANGELOS STAVROU ◽  
JEFFREY S. KATZ ◽  
DIANA KELLEY ◽  
...  
Keyword(s):  
Smart Grid ◽  
Control Systems ◽  
Leadership Competencies ◽  
Cyber Attacks ◽  
Industrial Control ◽  
Public And Private ◽  
Industrial Control Systems ◽  
Grid Systems ◽  
Private Organizations ◽  
Emerging Risk

The extensive integration of interconnected devices and the inadvertent information obtained from untrusted sources has exposed the Industrial Control Systems (ICS) ecosystem to remote attacks by the exploitation of new and old vulnerabilities. Unfortunately, although recognized as an emerging risk based on the recent rise of cyber attacks, cybersecurity for ICS has not been addressed adequately both in terms of technology but, most importantly, in terms of organizational leadership and policy. In this paper, we will present our findings regarding the cybersecurity challenges for Smart Grid and ICS and the need for changes in the way that organizations perceive cybersecurity risk and leverage resources to balance the needs for information security and operational security. Moreover, we present empirical data that point to cybersecurity governance and technology principles that can help public and private organizations to navigate successfully the technical cybersecurity challenges for ICS and Smart Grid systems. We believe that by identifying and mitigating the inherent risks in their systems, operations, and processes, enterprises will be in a better position to shield themselves and protect against current and future cyber threats.

scholarly journals Towards the Smart Grid: Substation Automation Architecture and Technologies

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
A. Leonardi ◽  
K. Mathioudakis ◽  
A. Wiesmaier ◽  
F. Zeiger
Keyword(s):  
Smart Grid ◽  
Control Systems ◽  
Distributed Generation ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Electrical Grid ◽  
Key Stakeholders ◽  
Substation Automation ◽  
New Applications ◽  
Transmission And Distribution

This paper deals with Industrial Control Systems (ICS) of the electrical sector and especially on the Smart Grid. This sector has been particularly active at establishing new standards to improve interoperability between all sector players, driven by the liberalization of the market and the introduction of distributed generation of energy. The paper provides a state-of-the-art analysis on architectures, technologies, communication protocols, applications, and information standards mainly focusing on substation automation in the transmission and distribution domain. The analysis shows that there is tremendous effort from the Smart Grid key stakeholders to improve interoperability across the different components managing an electrical grid, from field processes to market exchanges, allowing the information flowing more and more freely across applications and domains and creating opportunity for new applications that are not any more constraint to a single domain.

Research of Smart Gird Functional Safety Certification

2012 ◽  
Vol 263-266 ◽  
pp. 3168-3173
Author(s):  
Xiu Li Huang ◽  
Tao Zhang ◽  
Yuan Yuan Ma ◽  
Yu Fei Wang ◽  
Ye Hua
Keyword(s):  
Control System ◽  
Smart Grid ◽  
Control Systems ◽  
Evaluation Process ◽  
Functional Safety ◽  
Industrial Control System ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Security Issues ◽  
Safety Certification

With the increasing number of attacks against industrial control systems, functions security issues of the smart grid industrial control system gradually become the focus of research in the field of smart grid. This paper introduced smart grid industrial control systems threats which faced and incidents firstly, and analyzed the current situation of the functional safety certification, including certification bodies, certification tools and certification classes; Then, researched the smart grid industrial control system functional safety certification, gave the content of the smart grid industrial control system functional safety certification, and made the design of its hardware evaluation process; Finally, we discuss the research status of functional safety certification, and pointed out the problems that the smart grid industrial control system functional safety faces.

scholarly journals Adversarial Campaign Mitigation via ROC-Centric Prognostics

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Javier Echauz ◽  
Keith Kenemer ◽  
Sarfaraz Hussein ◽  
Jay Dhaliwal ◽  
Saurabh Shintre ◽  
...  
Keyword(s):  
Control Systems ◽  
Domain Adaptation ◽  
Detection Problem ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
The Past ◽  
Deep Learning Model ◽  
Adaptation Layer ◽  
Machine Learning Models ◽  
Design Guidance

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of adversarial ML is still considered alchemy, with no practical unbroken defenses demonstrated to date, leaving PHM practitioners with few meaningful ways of addressing the problem. We introduce “turbidity detection” as a practical superset of the adversarial input detection problem, coping with adversarial campaigns rather than statistically invisible one-offs. This perspective is coupled with ROC-theoretic design guidance that prescribes an inexpensive domain adaptation layer at the output of a deep learning model during an attack campaign. The result aims to approximate the Bayes optimal mitigation that ameliorates the detection model’s degraded health. A proactively reactive type of prognostics is achieved via Monte Carlo simulation of various adversarial campaign scenarios, by sampling from the model’s own turbidity distribution to quickly deploy the correct mitigation during a real-world campaign.

scholarly journals Cyber Safety System by Implementing Integration of Expert Learning and Data Mining Concepts

2019 ◽  
Vol 8 (12S) ◽  
pp. 155-158
Keyword(s):  
Control Systems ◽  
Cyber Security ◽  
Detection System ◽  
Cyber Attacks ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Essential Information ◽  
Continuous Growth ◽  
Cyber Safety ◽  
The Cost

the continuous growth in the rate of cyber-attacks in recent years uplifts the worry for the cyber security of industrial control systems. The current efforts of the cyber security system are depended on firewalls, data diodes and other basic methods for prevention of infringement. A cyber threat, intrusion or infringement detection system detects malicious or noxious activities by scanning a system and investigate digitally by employing “machine learning” and “data digging” techniques for handling dynamic and complex functioning of malicious assaults in computer systems and extracting essential information from an input data. In this research paper, the techniques we have used to complete this research may bring advancement in recognition rates, decrease the fault rate which also led to a decrease in the cost factor..

A Study of Identifying Attacks on Industry Internet of Things Using Machine Learning

2021 ◽  
Author(s):  
Chia-Mei Chen ◽  
Zheng-Xun Cai ◽  
Gu-Hsin Lai
Keyword(s):  
Internet Of Things ◽  
Control Systems ◽  
Classification Model ◽  
Multiple Sources ◽  
Industrial Control ◽  
Critical Function ◽  
Industrial Control Systems ◽  
Detection Model ◽  
The Past ◽  
Industrial Companies

The “Industry 4.0” revolution and Industry Internet of Things (IIoT) has dramatically transformed how manufacturing and industrial companies operate. Industrial control systems (ICS) process critical function, and the past ICS attacks have caused major damage and disasters in the communities. IIoT devices in an ICS environment communicate in heterogeneous protocols and the attack vectors might exhibit different misbehavior patterns. This study proposes a classification model to detect anomalies in ICS environments. The evaluation has been conducted by using ICS datasets from multiple sources and the results show that the proposed LSTM detection model performs effectively.

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