Exploratory security analytics for anomaly detection. Issue 56 (February 2016)

• Record Type: Journal Article
• Authors: Pierazzi, Fabio; Casolari, Sara; Colajanni, Michele; Marchetti, Mirco
• Is Part Of: Computers & security. Issue 56(2016)
• Page Start: 28
• Page End: 49
• Publication Date: 2016-02
• Subjects: Security analytics -- Network alerts -- Temporal characterization -- Time series analysis -- Anomaly detection; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2015.10.003
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: The huge number of alerts generated by network-based defense systems prevents detailed manual inspections of security events. Existing proposals for automatic alerts analysis work well in relatively stable and homogeneous environments, but in modern networks, that are characterized by extremely complex and dynamic behaviors, understanding which approaches can be effective requires exploratory data analysis and descriptive modeling. We propose a novel framework for automatically investigating temporal trends and patterns of security alerts with the goal of understanding whether and which anomaly detection approaches can be adopted for identifying relevant security events. Several examples referring to a real large network show that, despite the high intrinsic dynamism of the system, the proposed framework is able to extract relevant descriptive statistics that allow to determine the effectiveness of popular anomaly detection approaches on different alerts groups.