Work

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

Year: 2019

Type: article

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.

Source: Security and Communication Networks

DOI: 10.1155/2019/1574749

Authors: Francisco Sales de Lima Filho, Frederico A. F. Silveira, Agostinho de Medeiros Brito Junior, Genoveva Vargas-Solar, Luiz F. Silveira

Institutions: Universidade Federal do Rio Grande do Norte, Centre National de la Recherche Scientifique, Laboratoire d'Informatique de Grenoble, Laboratoire franco-mexicain d'informatique et d'automatique

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,670

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Universidade Federal do Rio Grande do Norte