TY - JOUR A2 - Maglaras, Leandros AU - Lima Filho, Francisco Sales de AU - Silveira, Frederico A. F. AU - de Medeiros Brito Junior, Agostinho AU - Vargas-Solar, Genoveva AU - Silveira, Luiz F. PY - 2019 DA - 2019/10/13 TI - Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning SP - 1574749 VL - 2019 AB - 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. SN - 1939-0114 UR - https://doi.org/10.1155/2019/1574749 DO - 10.1155/2019/1574749 JF - Security and Communication Networks PB - Hindawi KW - ER -