A SCADA System for Energy Management in Intelligent Buildings

This paper develops an energy management platform for intelligent buildings using a SCADA system (Supervisory Control And Data Acquisition). This SCADA system integrates different types of information coming from the several technologies present in modern buildings (control of ventilation, temperature, illumination, etc.). The developed control strategy implements an hierarchical cascade controller where inner loops are performed by local PLCs (Programmable Logic Controller), and the outer loop is managed by a centralized SCADA system, which interacts with the entire local PLC network. In this paper a Predictive Controller is implemented above the centralized SCADA platform. Tests applied to the control of temperature and luminosity in huge-area rooms are presented. The developed Predictive Controller optimizes the satisfaction of user explicit preferences coming from several distributed user-interfaces, subjected to the overall constraints of energy waste minimization. In order to run the Predictive Controller with the SCADA platform a communication channel was developed to allow communication between the SCADA system and the MATLAB application where the Predictive Controller runs

Front-End electronics configuration system for CMS

The four LHC experiments at CERN have decided to use a commercial SCADA (Supervisory Control And Data Acquisition) product for the supervision of their DCS (Detector Control System). The selected SCADA, which is therefore used for the CMS DCS, is PVSS II from the company ETM. This SCADA has its own database, which is suitable for storing conventional controls data such as voltages, temperatures and pressures. In addition, calibration data and FE (Front-End) electronics configuration need to be stored. The amount of these data is too large to be stored in the SCADA database [1]. Therefore an external database will be used for managing such data. However, this database should be completely integrated into the SCADA framework, it should be accessible from the SCADA and the SCADA features, e.g. alarming, logging should be benefited from. For prototyping, Oracle 8i was selected as the external database manager. The development of the control system for calibration constants and FE electronics configuration has been done in close collaboration with the CMS tracker group and JCOP (Joint COntrols Project)(1). (1)The four LHC experiments and the CERN IT/CO group has merged their efforts to build the experiments controls systems and set up the JCOP at the end of December, 1997 for this purpose.Comment: 3 pages, 4 figures, Icaleps'01 conference PSN WEDT00

Incident Analysis & Digital Forensics in SCADA and Industrial Control Systems

SCADA and industrial control systems have been traditionally isolated in physically protected environments. However, developments such as standardisation of data exchange protocols and increased use of IP, emerging wireless sensor networks and machine-to-machine communication mean that in the near future related threat vectors will require consideration too outside the scope of traditional SCADA security and incident response. In the light of the significance of SCADA for the resilience of critical infrastructures and the related targeted incidents against them (e.g. the development of stuxnet), cyber security and digital forensics emerge as priority areas. In this paper we focus on the latter, exploring the current capability of SCADA operators to analyse security incidents and develop situational awareness based on a robust digital evidence perspective. We look at the logging capabilities of a typical SCADA architecture and the analytical techniques and investigative tools that may help develop forensic readiness to the level of the current threat environment requirements. We also provide recommendations for data capture and retention

Graphical user interface (GUI) for supervisory control of computer intergtated manufacturing (CIM-70A) using SCADA

Supervisory Control system and the Acquisition Data or SCADA is generalization of effective plant monitoring and conU'ol system in meeting production needs etc. The aim of the study is to prepare a SCADA system for AS/RS, functional Mechatronics Educational Material which simulates to real-life production system. Graphical control buttons to the system will be design to perform single or multiple tasks. The software is form Citect Pty. Limited called Citect SCADA. This project will be discussed as it applied in a CIM-70A at Mechatronic Laboratory of UTHM. Designing a controlling and monitoring system not only for AS/RS but it is also a way providing up-to-date data. It will provide system operators with central or local control using clear, concise, resizable graphics pages (screens). Graphical control buttons to the system will be design to perform single or multiple tasks. In the last chapter, some methodologies for solving the problem as well as to improve the SCADA are proposed

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

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

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