We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning.
- Authors
Lima Filho, Francisco Sales de; Silveira, Frederico A. F.; de Medeiros Brito Junior, Agostinho; Vargas-Solar, Genoveva; Silveira, Luiz F.
- Abstract
Users and Internet service providers (ISPs) are constantly affected by denial-of-service (DoS) attacks. This cyber threat continues to grow even with the development of new protection technologies. Developing mechanisms to detect this threat is a current challenge in network security. This article presents a machine learning- (ML-) based DoS detection system. The proposed approach makes inferences based on signatures previously extracted from samples of network traffic. The experiments were performed using four modern benchmark datasets. The results show an online detection rate (DR) of attacks above 96%, with high precision (PREC) and low false alarm rate (FAR) using a sampling rate (SR) of 20% of network traffic.
- Subjects
DENIAL of service attacks; MACHINE learning; INTERNET service providers; COMPUTER network security; FALSE alarms; INTERNET traffic
- Publication
Security & Communication Networks, 2019, p1
- ISSN
1939-0114
- Publication type
Article
- DOI
10.1155/2019/1574749