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. Such log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity, confidentiality and availability. On this basis, we propose a parameter-free method 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 particular, we explore and validate the application of our method 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.
Keywords: intrusion detection systems; anomaly detection; normalized compression distance; text mining; data-driven security
This research has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 872855 (TRESCA project), from the Comunidad de Madrid (Spain) under the projects CYNAMON (P2018/TCS-4566) and S2017/BMD-3688, co-financed with FSE and FEDER EU funds, by the Consejo Superior de Investigaciones Científicas (CSIC) under the project LINKA20216 (“Advancing in cybersecurity technologies”, i-LINK+ program), and by Spanish project MINECO/FEDER TIN2017-84452-R.