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Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning

2019; Hindawi Publishing Corporation; Volume: 2019; Linguagem: Inglês

10.1155/2019/1574749

1939-0114

Francisco Sales de Lima Filho, Frederico Augusto Fernandes Silveira, Agostinho de Medeiros Brito, Genoveva Vargas‐Solar, Luiz F. Q. Silveira,

Advanced Malware Detection Techniques

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.