Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
| العنوان: | Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments |
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| المؤلفون: | Mario Lemes Proença, Jaime Lloret, Matheus P. Novaes, Luiz F. Carvalho |
| المصدر: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| بيانات النشر: | Elsevier BV, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer Networks and Communications, Computer science, Denial-of-service attack, 02 engineering and technology, Computer security, computer.software_genre, SDN, Adversarial system, Deep Learning, Control theory, 0202 electrical engineering, electronic engineering, information engineering, Forwarding plane, Control logic, business.industry, Deep learning, Adversarial attacks, 020206 networking & telecommunications, INGENIERIA TELEMATICA, GAN, Hardware and Architecture, 020201 artificial intelligence & image processing, Anomaly detection, Artificial intelligence, DDoS, business, Software-defined networking, computer, Software |
| الوصف: | [EN] Over the last few years, Software Defined Networking (SDN) paradigm has become an emerging architecture to design future networks and to meet new application demands. SDN provides resources for improving network control and management by separating control and data plane, and the logical control is centralized in a controller. However, the centralized control logic can be an ideal target for malicious attacks, mainly Distributed Denial of Service (DDoS) attacks. Recently, Deep Learning has become a powerful technique applied in cybersecurity, and many Network Intrusion Detection (NIDS) have been proposed in recent researches. Some studies have indicated that deep neural networks are sensitive in detecting adversarial attacks. Adversarial attacks are instances with certain perturbations that cause deep neural networks to misclassify. In this paper, we proposed a detection and defense system based on Adversarial training in SDN, which uses Generative Adversarial Network (GAN) framework for detecting DDoS attacks and applies adversarial training to make the system less sensitive to adversarial attacks. The proposed system includes well-defined modules that enable continuous traffic monitoring using IP flow analysis, enabling the anomaly detection system to act in near-real-time. We conducted the experiments on two distinct scenarios, with emulated data and the public dataset CICDDoS 2019. Experimental results demonstrated that the system efficiently detected up-to-date common types of DDoS attacks compared to other approaches. This work has been partially supported by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grant of Project 310668/2019-0 and by SETI, Brazil/Fundacao Araucaria due to the concession of scholarships; by the "Ministerio de Economia y Competitividad, Spain"in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento"within the project under Grant TIN2017-84802-C2-1-P. |
| وصف الملف: | application/pdf |
| تدمد: | 0167-739X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fcb538490c1b5aeb6a6692e021ae69d https://doi.org/10.1016/j.future.2021.06.047 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....0fcb538490c1b5aeb6a6692e021ae69d |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 0167739X |
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