We investigate the consequence of failures, occurring on the electrical grid, on a telecommunication network. We have focused on the Italian electrical transmission network and the backbone of the internet network for research (GARR). Electrical network has been simulated using the DC power flow method; data traffic on GARR by a model of the TCP/IP basic features. The status of GARR nodes has been related to the power level of the (geographically) neighbouring electrical nodes (if the power level of a node is lower than a threshold, all communication nodes depending on it are switched off). The electrical network has been perturbed by lines removal: the consequent re-dispatching reduces the power level in all nodes. This reduces the number of active GARR nodes and, thus, its Quality of Service (QoS). Averaging over many configurations of perturbed electrical network, we have correlated the degradation of the electrical network with that of the communication network. Results point to a sizeable amplification of the effects of faults on the electrical network on the communication network, also in the case of a moderate coupling between the two networks.

This paper presents a dataset to support researchers in the validation process of solutions such as Intrusion Detection Systems (IDS) based on artificial intelligence and machine learning techniques for the detection and categorization of threats in Cyber Physical Systems (CPS). To this end, data were acquired from a hardware-in-the-loop Water Distribution Testbed (WDT) which emulates water flowing between eight tanks via solenoid-valves, pumps, pressure and flow sensors. The testbed is composed of a real subsystem that is virtually connected to a simulated one. The proposed dataset encompasses both physical and network data in order to highlight the consequences of attacks in the physical process as well as in network traffic behaviour. Simulations data are organized in four different acquisitions for a total duration of 2 hours by considering normal scenario and multiple anomalies due to cyber and physical attacks.

Welfare in developed countries strongly relies on many heterogeneous infrastructures generically named as critical infrastructures. These infrastructures, designed as autonomous systems, are actually more and more mutually dependent. This introduces new and extremely dangerous vulnerabilities in the overall system because an accidental or a malicious fault (e.g. terroristic attack) could exploit these 'connections'to unpredictably spread, amplifying its negative consequences and affecting unforeseeable and haphazard sets of users. In this paper, we analyse performance degradation induced on this system of systems by the presence and spreading of failures in order to emphasise the most critical links existing among different phenomena. Due to uncertainties that characterise these systems, we use Fuzzy Numbers (FNs) to represent involved quantities. This allows a modelling approach that can be set up using also qualitative information that are easier to obtain from experts and stakeholders. Moreover, this choice brings to a better characterisation of the level of confidence of our results. Preliminary results on a simple case study illustrate the effectiveness of the proposed approach.