  Nowadays, information and communications technology systems are fundamental
assets of our social and economical model, and thus they should be properly
protected against the malicious activity of cybercriminals. Defence mechanisms
are generally articulated around tools that trace and store information in
several ways, the simplest one being the generation of plain text files coined
as security logs. This log files are usually inspected, in a semi-automatic
way, by security analysts to detect events that may affect system integrity. On
this basis, we propose a parameter-free methodology to detect security
incidents from structured text regardless its nature. We use the Normalized
Compression Distance to obtain a set of features that can be used by a Support
Vector Machine to classify events from a heterogeneous cybersecurity
environment. In specific, we explore and validate the application of our
methodology in four different cybersecurity domains: HTTP anomaly
identification, spam detection, Domain Generation Algorithms tracking and
sentiment analysis. The results obtained show the validity and flexibility of
our approach in different security scenarios with a low configuration burden.
