SCADA Security. Xun Yi
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in the market that describe the various SCADA‐based unsupervised intrusion detection methods; they are, however, relatively unfocused and lacking much details on the methods for SCADA systems in terms of detection approaches, implementation, data collection, evaluation, and intrusion response. Briefly, this book provides the reader with the tools that are intended to provide practical development and implementation of SCADA security in general. Moreover, this book introduces solutions to practical problems that SCADA intrusion detection systems experience when building unsupervised intrusion detection methods from unlabeled data. The major challenge was to bring various aspects of SCADA intrusion detection systems, such as building unsupervised anomaly detection methods and evaluating their respective performance, under a single umbrella.
The target audience of this book is composed of professionals and researchers working in the field of SCADA security. At the same time, it can be used by researchers who could be interested in SCADA security in general and building SCADA unsupervised intrusion detection systems in particular. Moreover, this book may aid them to gain an overview of a field that is still largely dominated by conference publications and a disparate body of literature.
The book has seven main chapters that are organized as follows. In Chapter 3, the book deals with the establishment of a SCADA security testbed that is a salient part for evaluating and testing the practicality and efficacy of any proposed SCADA security solution. This is because the evaluation and testing using actual SCADA systems are not feasible since their availability and performance are most likely to be affected. Chapter 4 looks in much more detail at the novel efficient k‐Nearest Neighbour approach based on Various‐Widths Clustering, named kNNVWC, to efficiently address the infeasibility of the use of the k‐nearest neighbour approach with large and high‐dimensional data. In Chapter 5, a novel SCADA Data‐Driven Anomaly Detection (SDAD) approach is described in detail. This chapter demonstrates the practicality of the clustering‐based method to extract proximity‐based detection rules that comprise a tiny portion compared to the training data, while meanwhile maintain the representative nature of the original data. Chapter 6 looks in detail at a novel promising approach, called GATUD (Global Anomaly Threshold to Unsupervised Detection), that can improve the accuracy of unsupervised anomaly detection approaches that are compliant with the following assumptions: (i) the number of normal observations in the data set vastly outperforms the abnormal observations and (ii) the abnormal observations must be statistically different from normal ones. Finally, Chapter 7 looks at the authentication protocols in SCADA systems, which enable secure communication between all the components of such systems. This chapter describes two efficient TPASS protocols for SCADA systems: one is built on two‐phase commitment and has lower computation complexity and the other is based on zero‐knowledge proof and has less communication rounds. Both protocols are particularly efficient for the client, who only needs to send a request and receive a response.
ACRONYMS
AGAAmerican Gas AssociationASCIIAmerican Standard Code for Information InterchangeCOTSCommercial‐Off‐The‐ShelfCORECommon Open Research EmulatorCRCCyclic Redundancy CheckDDLDynamic Link LibraryDNPDistributed Network ProtocolDOSDenial Of ServiceEDMMEnsemble‐based Decision‐Making ModelEk‐NNExhaustive k‐Nearest NeighborEMANEExtendable Mobile Ad‐hoc Network EmulatorEPANETEnvironmental Protection Agency NetworkFEPFront End ProcessorGATUDGlobal Anomaly Threshold to Unsupervized DetectionHMIHuman Machine Interfacek‐NNk‐Nearest NeighborkNNVWCk‐NN based on Various‐Widths ClusteringIDSIntrusion Detection SystemIEDIntelligent Electronic DeviceIPInternet ProtocolITInformation TechnologyLANLocal Area NetworkNISCCNational Infrastructure Security Coordination CenterNS2Network Simulator 2NS3Network Simulator 3OMNETObjective Modular Network TestbedOPNETOptimized Network Engineering ToolOSTOrthogonal Structure TreeOSVDBOpen Source Vulnerability DataBasePCAPrincipal Component AnalysisPLCProgrammable Logic ControllerPLSPartial Least SquaresRTURemote Terminal UnitSCADASupervisory Control And Data AcquisitionSCADAVTSCADA security testbed based on Virtualization TechnologySDADSCADA Data‐driven Anomaly DetectionTCPTransmission Control ProtocolTPASSThreshold Password‐Authenticated Secret S in the boo.. It is haringUDPUser Datagram ProtocolUSBUniversal Serial Bus
CHAPTER 1 Introduction
This aim of this introductory chapter is to motivate the extensive research work carried in this book, highlighting the existing solutions and their limitations, and putting in context the innovative work and ideas described in this book.
1.1 Overview
Supervisory Control and Data Acquisition (SCADA) systems have been integrated to control and monitor industrial processes and our daily critical infrastructures such as electric power generation, water distribution and waste water collection systems. This integration adds valuable input to improve the safety of the process and the personnel and to reduce operation costs (Boyer, 2009). However, any disruption to SCADA systems can result in financial disasters or may lead to loss of life in a worst case scenario. Therefore, in the past, such systems were secure by virtue of their isolation and only proprietary hardware and software were used to operate these systems. In other words, these systems were self‐contained and totally isolated from the public network (e.g., the Internet). This isolation created the myth that malicious intrusions and attacks from the outside world were not a big concern and that such attacks were expected to come from the inside. Therefore, when developing SCADA protocols, the security of the information system was given no consideration.
In recent years, SCADA systems have begun to shift away from using proprietary and customized hardware and software to using Commercial‐Off‐The‐Shelf (COTS) solutions. This shift has increased their connectivity to the public networks using standard protocols (e.g., TCP/IP). In addition, there is decreased reliance on a single vendor. Undoubtedly, this increases productivity and profitability but will, however, expose these systems to cyber threats (Oman et al., 2000). According to a survey published by the SANS Institute (Bird and Kim, 2012), only 14% of organizations carry out security reviews of COTS applications that are being used, while over 50% of other organizations do not perform security assessments and rely only on vendor reputation or the legal liability agreements, or they have no policies at all regarding the use of COTS solutions.
The adoption of COTS solutions is a time‐ and cost‐efficient means of building SCADA systems. In addition, COST‐based devices are intended to operate on traditional Ethernet networks and the TCP/IP stack. This feature allows devices from various vendors to communicate with each other, and also helps to remotely supervise and control critical industrial systems from any place and at any time using the Internet. Moreover, wireless technologies can efficiently be used to provide mobility and local control for multivendor devices at a low cost for installation and maintenance. However, the convergence of state‐of‐the‐art communication technologies exposes SCADA systems to all the inherent vulnerabilities of these technologies. In what follows, we discuss how the potential cyber‐attacks against traditional IT can also be possible against SCADA systems.
Denial of Services (DoS) attacks. This is a potential attack on any Internet‐connected device where a large number of spurious packets are sent to a victim in order to consume excessive amounts of endpoint network bandwidth. A packet flooding attack (Houle et al., 2001) is often used as another term for a DoS attack. This type of attack delays or totally prevents the victim from receiving the legitimate packets (Householder et al., 2001). SCADA networking devices that are exposed to the Internet such as routers, gateways and firewalls are susceptible to this type of attack. Long et al. (2005) proposed two models of DoS attacks on a SCADA network using reliable simulation. The first model was directly launched to an endpoint