9,726 research outputs found
Statistical Methods for Detection and Mitigation of the Effect of Different Types of Cyber-Attacks and Inconsistencies in Electrical Design Parameters in a Real World Distribution System
In the present grid real time control systems are the energy management systems and distribution management systems that utilize measurements from real-time units (RTUs) and Supervisory Control and Data Acquisition (SCADA). The SCADA systems are designed to operate on isolated, private networks without even basic security features which are now being migrated to modern IP-based communications providing near real time information from measuring and controlling units. To function brain (SCADA) properly heart (RTUs) should provide necessary response thereby creating a coupling which makes SCADA systems as targets for cyber-attacks to cripple either part of the electric transmission grid or fully shut down (create blackout) the grid. Cyber-security research for a distribution grid is a topic yet to be addressed. To date firewalls and classic signature-based intrusion detection systems have provided access control and awareness of suspicious network traffic but typically have not offered any real-time detection and defense solutions for electric distribution grids.;This thesis work not only addresses the cyber security modeling, detection and prevention but also addresses model inconsistencies for effectively utilizing and controlling distribution management systems. Inconsistencies in the electrical design parameters of the distribution network or cyber-attack conditions may result in failing of the automated operations or distribution state estimation process which might lead the system to a catastrophic condition or give erroneous solutions for the probable problems. This research work also develops a robust and reliable voltage controller based on Multiple Linear Regression (MLR) to maintain the voltage profile in a smart distribution system under cyber-attacks and model inconsistencies. The developed cyber-attack detection and mitigation algorithms have been tested on IEEE 13 node and 600+ node real American electric distribution systems modeled in Electric Power Research Institute\u27s (EPRI) OpenDSS software
Towards a Layered Architectural View for Security Analysis in SCADA Systems
Supervisory Control and Data Acquisition (SCADA) systems support and control
the operation of many critical infrastructures that our society depend on, such
as power grids. Since SCADA systems become a target for cyber attacks and the
potential impact of a successful attack could lead to disastrous consequences
in the physical world, ensuring the security of these systems is of vital
importance. A fundamental prerequisite to securing a SCADA system is a clear
understanding and a consistent view of its architecture. However, because of
the complexity and scale of SCADA systems, this is challenging to acquire. In
this paper, we propose a layered architectural view for SCADA systems, which
aims at building a common ground among stakeholders and supporting the
implementation of security analysis. In order to manage the complexity and
scale, we define four interrelated architectural layers, and uses the concept
of viewpoints to focus on a subset of the system. We indicate the applicability
of our approach in the context of SCADA system security analysis.Comment: 7 pages, 4 figure
Securing the Participation of Safety-Critical SCADA Systems in the Industrial Internet of Things
In the past, industrial control systems were ‘air gapped’ and
isolated from more conventional networks. They used
specialist protocols, such as Modbus, that are very different
from TCP/IP. Individual devices used proprietary operating
systems rather than the more familiar Linux or Windows.
However, things are changing. There is a move for greater
connectivity – for instance so that higher-level enterprise
management systems can exchange information that helps
optimise production processes. At the same time, industrial
systems have been influenced by concepts from the Internet
of Things; where the information derived from sensors and
actuators in domestic and industrial components can be
addressed through network interfaces. This paper identifies a
range of cyber security and safety concerns that arise from
these developments. The closing sections introduce potential
solutions and identify areas for future research
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
This paper presents the development of a Supervisory Control and Data
Acquisition (SCADA) system testbed used for cybersecurity research. The testbed
consists of a water storage tank's control system, which is a stage in the
process of water treatment and distribution. Sophisticated cyber-attacks were
conducted against the testbed. During the attacks, the network traffic was
captured, and features were extracted from the traffic to build a dataset for
training and testing different machine learning algorithms. Five traditional
machine learning algorithms were trained to detect the attacks: Random Forest,
Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained
machine learning models were built and deployed in the network, where new tests
were made using online network traffic. The performance obtained during the
training and testing of the machine learning models was compared to the
performance obtained during the online deployment of these models in the
network. The results show the efficiency of the machine learning models in
detecting the attacks in real time. The testbed provides a good understanding
of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin
Autonomic computing meets SCADA security
© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security
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