The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning
Open accessAbstract
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.
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Network Intrusion Detection for IoT Security Based on Learning Techniques
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Metadata
- Authors
- Lima Filho, F. S. D., Silveira, F. A. F., de Medeiros Brito Junior, A., Vargas-Solar, G., & Silveira, L. F.
- Venue
- Security and Communication Networks
- Publisher
- Wiley
- Published
- 2019-10-13
- Volume
- 2019
- Pages
- 1-15
- ISSN
- 1939-0114 (ISSN-L)
- 1939-0122
- OpenAlex
- W2979323854
- Cited by
- 293
- References
- 24
- FWCI
- 18.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Lima Filho, F. S. D., Silveira, F. A. F., de Medeiros Brito Junior, A., Vargas-Solar, G., & Silveira, L. F. (2019). Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning. Security and Communication Networks. https://doi.org/10.1155/2019/1574749
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