Prevention and Fighting against Web Attacks through Anomaly Detection Technology. A Systematic Review

• Published in: Sustainability 2020-06, Vol.12 (12), p.4945
• Main Authors: Sureda Riera, Tomás, Bermejo Higuera, Juan-Ramón, Bermejo Higuera, Javier, Martínez Herraiz, José-Javier, Sicilia Montalvo, Juan-Antonio
• Format: Article
• Language: English
• Subjects: Anomalies; Cloud computing; Datasets; Deep learning; International organizations; Intrusion detection systems; Literature reviews; Methods; Prevention; Reviews; Statistical analysis; Studies; Sustainability; Systematic review
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su12124945

Numerous techniques have been developed in order to prevent attacks on web servers. Anomaly detection techniques are based on models of normal user and application behavior, interpreting deviations from the established pattern as indications of malicious activity. In this work, a systematic review of the use of anomaly detection techniques in the prevention and detection of web attacks is undertaken; in particular, we used the standardized method of a systematic review of literature in the field of computer science, proposed by Kitchenham. This method is applied to a set of 88 papers extracted from a total of 8041 reviewed papers, which have been published in notable journals. This paper discusses the process carried out in this systematic review, as well as the results and findings obtained to identify the current state of the art of web anomaly detection.