  Nowadays, the Security Information and Event Management (SIEM) systems take
on great relevance in handling security issues for critical infrastructures as
Internet Service Providers. Basically, a SIEM has two main functions: i) the
collection and the aggregation of log data and security information from
disparate network devices (routers, firewalls, intrusion detection systems, ad
hoc probes and others) and ii) the analysis of the gathered data by
implementing a set of correlation rules aimed at detecting potential suspicious
events as the presence of encrypted real-time traffic. In the present work, the
authors propose an enhanced implementation of a SIEM where a particular focus
is given to the detection of encrypted Skype traffic by using an ad-hoc
developed enhanced probe (ESkyPRO) conveniently governed by the SIEM itself.
Such enhanced probe, able to interact with an agent counterpart deployed into
the SIEM platform, is designed by exploiting some machine learning concepts.
The main purpose of the proposed ad-hoc SIEM is to correlate the information
received by ESkyPRO and other types of data obtained by an Intrusion Detection
System (IDS) probe in order to make the encrypted Skype traffic detection as
accurate as possible.
