The Network Function Virtualization (NFV) paradigm has been devised as an enabler of next generation network infrastructures by speeding up the provisioning and the composition of novel network services. The latter are implemented via a chain of virtualized network functions, a process known as Service Function Chaining. In this paper, we evaluate the availability of multi-tenant SFC infrastructures, where every network function is modeled as a multi-state system and is shared among different and independent tenants. To this aim, we propose a Universal Generating Function (UGF) approach, suitably extended to handle performance vectors, that we call Multidimensional UGF. This novel methodology is validated in a realistic multi-tenant telecommunication network scenario, where the service chain is composed by the network elements of an IP Multimedia Subsystem implemented via NFV. A steady-state availability evaluation of such an exemplary system is presented and a redundancy optimization problem is solved, so providing the SFC infrastructure which minimizes deployment cost while respecting a given availability requirement.

Distributed Denial-of-Service (DDoS) attacks are usually launched through the botnet, an "army" of compromised nodes hidden in the network. Inferential tools for DDoS mitigation should accordingly enable an early and reliable discrimination of the normal users from the compromised ones. Unfortunately, the recent emergence of attacks performed at the application layer has multiplied the number of possibilities that a botnet can exploit to conceal its malicious activities. New challenges arise, which cannot be addressed by simply borrowing the tools that have been successfully applied so far to earlier DDoS paradigms. In this work, we offer basically three contributions: i) we introduce an abstract model for the aforementioned class of attacks, where the botnet emulates normal traffic by continually learning admissible patterns from the environment; ii) we devise an inference algorithm that is shown to provide a consistent (i.e., converging to the true solution as time elapses) estimate of the botnet possibly hidden in the network; and iii) we verify the validity of the proposed inferential strategy over real network traces.

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