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

• Published in: Security and communication networks Vol. 2019; no. 2019; pp. 1 - 15
• Main Authors: Vargas-Solar, Genoveva, de Medeiros Brito Junior, Agostinho, Silveira, Frederico A. F., Lima Filho, Francisco Sales de, Silveira, Luiz F. Q.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 13.10.2019; Hindawi; John Wiley & Sons, Inc; John Wiley & Sons, Ltd
• Subjects: Communications traffic; Computer Science; Denial of service attacks; False alarms; Internet of Things; Internet service providers; Machine learning; Security management; Software; Wireless networks
• ISSN: 1939-0114; 1939-0122; 1939-0122
• DOI: 10.1155/2019/1574749

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.