Quantitative dependability and interdependency models for large-scale cyber-physical systems

Cyber-physical systems link cyber infrastructure with physical processes through an integrated network of physical components, sensors, actuators, and computers that are interconnected by communication links. Modern critical infrastructures such as smart grids, intelligent water distribution networks, and intelligent transportation systems are prominent examples of cyber-physical systems. Developed countries are entirely reliant on these critical infrastructures, hence the need for rigorous assessment of the trustworthiness of these systems. The objective of this research is quantitative modeling of dependability attributes -- including reliability and survivability -- of cyber-physical systems, with domain-specific case studies on smart grids and intelligent water distribution networks. To this end, we make the following research contributions: i) quantifying, in terms of loss of reliability and survivability, the effect of introducing computing and communication technologies; and ii) identifying and quantifying interdependencies in cyber-physical systems and investigating their effect on fault propagation paths and degradation of dependability attributes. Our proposed approach relies on observation of system behavior in response to disruptive events. We utilize a Markovian technique to formalize a unified reliability model. For survivability evaluation, we capture temporal changes to a service index chosen to represent the extent of functionality retained. In modeling of interdependency, we apply correlation and causation analyses to identify links and use graph-theoretical metrics for quantifying them. The metrics and models we propose can be instrumental in guiding investments in fortification of and failure mitigation for critical infrastructures. To verify the success of our proposed approach in meeting these goals, we introduce a failure prediction tool capable of identifying system components that are prone to failure as a result of a specific disruptive event. Our prediction tool can enable timely preventative actions and mitigate the consequences of accidental failures and malicious attacks --Abstract, page iii

Development of a Security Methodology for Cooperative Information Systems: The CooPSIS Project

Since networks and computing systems are vital components of today\u27s life, it is of utmost importance to endow them with the capability to survive physical and logical faults, as well as malicious or deliberate attacks. When the information system is obtained by federating pre-existing local systems, a methodology is needed to integrate security policies and mechanisms under a uniform structure. Therefore, in building distributed information systems, a methodology for analysis, design and implementation of security requirements of data and processes is essential for obtaining mutual trust between cooperating organizations. Moreover, when the information system is built as a cooperative set of e-services, security is related to the type of data, to the sensitivity context of the cooperative processes and to the security characteristics of the communication paradigms. The CoopSIS (Cooperative Secure Information Systems) project aims to develop methods and tools for the analysis, design, implementation and evaluation of secure and survivable distributed information systems of cooperative type, in particular with experimentation in the Public Administration Domain. This paper presents the basic issues of a methodology being conceived to build a trusted cooperative environment, where data sensitivity parameters and security requirements of processes are taken into account. The milestones phases of the security development methodology in the context of this project are illustrated

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii

Modelling Security of Critical Infrastructures: A Survivability Assessment

Critical infrastructures, usually designed to handle disruptions caused by human errors or random acts of nature, define assets whose normal operation must be guaranteed to maintain its essential services for human daily living. Malicious intended attacks to these targets need to be considered during system design. To face these situations, defence plans must be developed in advance. In this paper, we present a Unified Modelling Language profile, named SecAM, that enables the modelling and security specification for critical infrastructures during the early phases (requirements, design) of system development life cycle. SecAM enables security assessment, through survivability analysis, of different security solutions before system deployment. As a case study, we evaluate the survivability of the Saudi Arabia crude-oil network under two different attack scenarios. The stochastic analysis, carried out with Generalized Stochastic Petri nets, quantitatively estimates the minimization of attack damages on the crude-oil network

Dependability analysis and recovery support for smart grids

The increasing scale and complexity of power grids exacerbate concerns about failure propagation. A single contingency, such as outage of a transmission line due to overload or weather-related damage, can cause cascading failures that manifest as blackouts. One objective of smart grids is to reduce the likelihood of cascading failure through the use of power electronics devices that can prevent, isolate, and mitigate the effects of faults. Given that these devices are themselves prone to failure, we seek to quantify the effects of their use on dependability attributes of smart grid. This thesis articulates analytical methods for analyzing two dependability attributes - reliability and survivability - and proposes a recovery strategy that limits service degradation. Reliability captures the probability of system-level failure; Survivability describes degraded operation in the presence of a fault. System condition and service capacity are selected as measures of degradation. Both reliability and survivability are evaluated using N-1 contingency analysis. Importance analysis is used to determine a recovery strategy that maintains the highest survivability in the course of the recovery process. The proposed methods are illustrated by application to the IEEE 9-bus test system, a simple model system that allows for clear articulation of the process. Simulation is used to capture the effect of faults in both physical components of the power grid and the cyber infrastructure that differentiates it as a smart grid --Abstract, page iii

A model-driven approach to survivability requirements assessment for critical systems

Survivability is a crucial property for those systems – such as critical infrastructures or military Command and Control Information Systems – that provide essential services, since the latter must be operational even when the system is compromised due to attack or faults. This article proposes a model-driven method and a tool –MASDES– to assess the survivability requirements of critical systems. The method exploits the use of (1) (mis)use case technique and UML profiling for the specification of the survivability requirements and (2) Petri nets and model checking techniques for the requirement assessment. A survivability assessment model is obtained from an improved specification of misuse cases, which encompasses essential services, threats and survivability strategies. The survivability assessment model is then converted into a Petri net model for verifying survivability properties through model checking. The MASDES tool has been developed within the Eclipse workbench and relies on Papyrus tool for UML. It consists of a set of plug-ins that enable (1) to create a survivability system view using UML and profiling techniques and (2) to verify survivability properties. In particular, the tool performs model transformations in two steps. First, a model-to-model transformation generates, from the survivability view, a Petri net model and properties to be checked in a tool-independent format. Second, model-to-text transformations produce the Petri net specifications for the model checkers. A military Command and Control Information Systems has been used as a case study to apply the method and to evaluate the MASDES tool, within an iterative-incremental software development process

Technology-related disasters:a survey towards disaster-resilient software defined networks

Resilience against disaster scenarios is essential to network operators, not only because of the potential economic impact of a disaster but also because communication networks form the basis of crisis management. COST RECODIS aims at studying measures, rules, techniques and prediction mechanisms for different disaster scenarios. This paper gives an overview of different solutions in the context of technology-related disasters. After a general overview, the paper focuses on resilient Software Defined Networks