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Article

Error-Robust Distributed Denial of Service Attack Detection Based on an Average Common Feature Extraction Technique

1
Department of Electrical Engineering, University of Brasília, Brasília 70910-900, Brazil
2
Department 2-Campus Lippstadt, Hamm-Lippstadt University of Applied Sciences, 59063 Hamm, Germany
3
Informatics Institute, Federal University of Rio Grande do Sul, Porto Alegre 91509-900, Brazil
4
Department of Mechanical Engineering, University of Brasília, Brasília 70910-900, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5845; https://doi.org/10.3390/s20205845
Received: 27 July 2020 / Revised: 10 September 2020 / Accepted: 18 September 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Smart Cities of the Future: A Cyber Physical System Perspective)
In recent years, advanced threats against Cyber–Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To face these challenges, this paper proposes a novel error-robust multidimensional technique for DDoS attack detection. By applying the well-known Higher Order Singular Value Decomposition (HOSVD), initially, the average value of the common features among instances is filtered out from the dataset. Next, the filtered data are forwarded to machine learning classification algorithms in which traffic information is classified as a legitimate or a DDoS attack. In terms of results, the proposed scheme outperforms traditional low-rank approximation techniques, presenting an accuracy of 98.94%, detection rate of 97.70% and false alarm rate of 4.35% for a dataset corruption level of 30% with a random forest algorithm applied for classification. In addition, for error-free conditions, it is found that the proposed approach outperforms other related works, showing accuracy, detection rate and false alarm rate of 99.87%, 99.86% and 0.16%, respectively, for the gradient boosting classifier. View Full-Text
Keywords: cyber–physical systems; machine learning; tensor decomposition; classification; error-robustness cyber–physical systems; machine learning; tensor decomposition; classification; error-robustness
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MDPI and ACS Style

Abreu Maranhão, J.P.; Carvalho Lustosa da Costa, J.P.; Pignaton de Freitas, E.; Javidi, E.; Timóteo de Sousa Júnior, R. Error-Robust Distributed Denial of Service Attack Detection Based on an Average Common Feature Extraction Technique. Sensors 2020, 20, 5845. https://doi.org/10.3390/s20205845

AMA Style

Abreu Maranhão JP, Carvalho Lustosa da Costa JP, Pignaton de Freitas E, Javidi E, Timóteo de Sousa Júnior R. Error-Robust Distributed Denial of Service Attack Detection Based on an Average Common Feature Extraction Technique. Sensors. 2020; 20(20):5845. https://doi.org/10.3390/s20205845

Chicago/Turabian Style

Abreu Maranhão, João P., João P. Carvalho Lustosa da Costa, Edison Pignaton de Freitas, Elnaz Javidi, and Rafael Timóteo de Sousa Júnior. 2020. "Error-Robust Distributed Denial of Service Attack Detection Based on an Average Common Feature Extraction Technique" Sensors 20, no. 20: 5845. https://doi.org/10.3390/s20205845

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