-The Systemic Risk is the risk that derives from the interdependence of the system under consideration, object of the analysis, and the services provided by other systems and, in general, by the interactions among them. The combination of the GOReM methodology and the RAMSoS method is proposed for Systemic Risk Assessment so as to provide the following benefits: (i) Effective modeling of SoSs structure and behavior; (ii) Explicit representation of dysfunctional behavior; (iii) Evaluation of different risk scenarios through agent-based simulation; (iv) Quantitative and qualitative risk assessment also in combination with classical analysis techniques (such as Bayesian Networks).
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Keywords—Cybersecurity, Modeling and Requirement Engineering, Systemic Risk Analysis Simulation, I.
IDEA AND PROPOSAL Identify the main phases of the Systemic Risk (SR)
Proposed a Modelling and Simulation based approach Defined a step by step methodology (not a software tool) Performing Static and Dynamic Systemic Risk Analysis II.
Figure 1): System Analysis, System Design and Simulation Modeling and Results Assessment.
System requirements and other aspects of interest are identified and described. The involved entities (such as stakeholders, services providers and so on) are identified along with their roles and related objectives. Goals to be achieved and their dependencies are highlighted. The rules and regulations that govern the context under analysis are identified.
Fig. 1. Systemic Risk Analysis Phases
The target of the analysis as well as boundaries of the design, i.e. what needs to be represented and what can or should be neglected/omitted, are defined. Specific use cases are redefined in terms of scenarios of interest. Application scenarios are introduced to specify the functionalities that should be provided in each business scenario description of the system is delivered by providing from different points of view such as for structural, functional, and so on.
C. Simulation Modeling & Results Evaluation
At this point, a subset of the models generated in the System Design macro-phase is selected and processed. According to the simulation-platform different Model-toModel transformation rules are defined. Great attention is placed on the indices / objectives identified during the System Analysis. From these indices and the objectives to be pursued, the simulation platform, which is able to support the desired analysis, is selected. Based on the objectives to be verified, it is possible to choose the simulation environment that better fits the type of analysis to be carried out.
DERIVING BAYESIAN NETWORKS MODELS FOR
SUPPORTING SYSTEMIC RISK ANALYSIS A. A combined approach for modeling and assessing the Systemic Risk
How and which entities of the overall system influence the operation of the entire system and the evaluation of the Systemic Risk.
Modeling and evaluating Systemic Risk by exploiting (agent-based) simulation + Bayesian Network B. RAMSoS and GOReM: Enabling Factors
Common modeling notation: SysML/UML.
Both RAMSoS and GOReM are defined in terms of phases and work-products GOReM is defined as a method to support the analysis of system requirements with particular emphasis on their elicitation and tracking; while RAMSoS is meant to be used mostly for supporting the validation and verification phases. Together they cover the entire Systemic Risk Analysis Phases
RISK ANALYSIS APPLIED TO A SERVICE OF ELECTRONIC
ONLINE PAYMENT OF POSTE ITALIANE
The risk of success or failure of the PEO service relies on two complementary services: SMS Notifications service (Mobile Service Provider) Payments and Transactions service (Web Service
Provider, Energy Provider, IT infrastructure) 1. A statistics based approach using a tool for a static analysis is applied: GeNIe (Graphical Network Interface) a development environment for the creation of decision models based on Bayesian Network (BN) 2. An agent-based approach using a dynamic tool is adopted: ReActor an object oriented framework based on discreteevents simulation
For each actor the following risk ranges (or QoS) have been identified: SMS Notification: Good, Low; Payments and Transactions: LowRisk, HighRisk; IT Internal Infrastructure: Good, Standard, Poor;
WebServiceProvider: High, Medium, Low;
MobileServiceProvider: HighLevelOfService,
StandardLevelOfService;
Once the model and relationships among actors and their goals are well described and defined, it is possible to use simulation to provide an assessment about what can happen into an application scenario according to specific inputs to the system. Figure 3 shows Architectural Modeling for risk analysis applied to a service of Electronic Online Payment of Poste Italiane.
Considering a combination of services based on high level quality percentage, the probability of PEO success is 99%, which means a LowRisk.
(i) the availability (working) or unavailability (not working) of a service
(ii) the time when the failure of a service happened (timestamps)
(iii) the cause of the failure, if it is due to internal or external factors.
This allows to assess the main system (PEO Service) and its interdependencies with the involved services, by considering events of faults and failures and their propagation in the network, from a dynamic point of view by including temporal constrains.