| Texto completo | |
| Autor(es): |
de Neira, Anderson Bergamini
;
Kantarci, Burak
;
Nogueira, Michele
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | Computer Networks; v. 222, p. 27-pg., 2023-01-14. |
| Resumo | |
Distributed Denial of Service (DDoS) attack is one of the biggest cyber threats. DDoS attacks have evolved in quantity and volume to evade detection and increase damage. Changes during the COVID-19 pandemic have left traditional perimeter-based security measures vulnerable to attackers that have diversified their activities by targeting health services, e-commerce, and educational services. DDoS attack prediction searches for signals of attack preparation to warn about the imminence of the attack. Prediction is necessary to handle high-volumetric DDoS attacks and to increase the time to defend against them. This survey article presents the classification of studies from the literature comprising the current state-of-the-art on DDoS attack prediction. It highlights the results of this extensive literature review categorizing the works by prediction time, architecture, employed methodology, and the type of data utilized to predict attacks. Further, this survey details each identified study and, finally, it emphasizes the research opportunities to evolve the DDoS attack prediction state-of-the-art. (AU) | |
| Processo FAPESP: | 18/23098-0 - MENTORED: da modelagem à experimentação - predizendo e detectando ataques DDoS e zero-day |
| Beneficiário: | Michele Nogueira Lima |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |