Examining the security of DDoS detection systems in software defined networks

Ahmed Abusnaina; Murat Yuksel; Dae Hun Nyang; Aziz Mohaisen

doi:10.1145/3360468.3368174

Examining the security of DDoS detection systems in software defined networks

Ahmed Abusnaina, Murat Yuksel, Dae Hun Nyang, Aziz Mohaisen

Department of Cyber Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

With the rapid development of Software-Defined Networking (SDN) advocating a centralized view of networks, efficient and reliable Distributed Denial of Service (DDoS) defenses are necessary to protect the centralized SDN controller. In this work, we explore the robustness of DL-based DDoS defenses in SDN against adversarial learning attacks. First, we investigate generic off-the-shelf adversarial attacks to test the robustness of DDoS defenses in SDN. Then, we propose Flow-Merge for realistic adversarial flows while achieving a high evasion rate. The evaluation shows that the proposed Flow-Merge is able to force the DL-based DDoS defenses to misclassify 100% of benign flows as malicious.

Original language	English
Title of host publication	CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019
Publisher	Association for Computing Machinery, Inc
Pages	49-50
Number of pages	2
ISBN (Electronic)	9781450370066
DOIs	https://doi.org/10.1145/3360468.3368174
State	Published - 9 Dec 2019
Event	15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019 - Orlando, United States Duration: 9 Dec 2019 → 12 Dec 2019

Publication series

Name	CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019

Conference

Conference	15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019
Country/Territory	United States
City	Orlando
Period	9/12/19 → 12/12/19

Bibliographical note

Publisher Copyright:
© 2019 held by the owner/author(s).

Keywords

Adversarial Attacks
Deep Learning
Distributed Denial of Service
Intrusion Detection

Access to Document

10.1145/3360468.3368174

Cite this

Abusnaina, A., Yuksel, M., Nyang, D. H., & Mohaisen, A. (2019). Examining the security of DDoS detection systems in software defined networks. In CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019 (pp. 49-50). (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3360468.3368174

Abusnaina, Ahmed ; Yuksel, Murat ; Nyang, Dae Hun et al. / Examining the security of DDoS detection systems in software defined networks. CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. Association for Computing Machinery, Inc, 2019. pp. 49-50 (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019).

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abstract = "With the rapid development of Software-Defined Networking (SDN) advocating a centralized view of networks, efficient and reliable Distributed Denial of Service (DDoS) defenses are necessary to protect the centralized SDN controller. In this work, we explore the robustness of DL-based DDoS defenses in SDN against adversarial learning attacks. First, we investigate generic off-the-shelf adversarial attacks to test the robustness of DDoS defenses in SDN. Then, we propose Flow-Merge for realistic adversarial flows while achieving a high evasion rate. The evaluation shows that the proposed Flow-Merge is able to force the DL-based DDoS defenses to misclassify 100\% of benign flows as malicious.",

keywords = "Adversarial Attacks, Deep Learning, Distributed Denial of Service, Intrusion Detection",

author = "Ahmed Abusnaina and Murat Yuksel and Nyang, \{Dae Hun\} and Aziz Mohaisen",

note = "Publisher Copyright: {\textcopyright} 2019 held by the owner/author(s).; 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

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Abusnaina, A, Yuksel, M, Nyang, DH & Mohaisen, A 2019, Examining the security of DDoS detection systems in software defined networks. in CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019, Association for Computing Machinery, Inc, pp. 49-50, 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019, Orlando, United States, 9/12/19. https://doi.org/10.1145/3360468.3368174

Examining the security of DDoS detection systems in software defined networks. / Abusnaina, Ahmed; Yuksel, Murat; Nyang, Dae Hun et al.
CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. Association for Computing Machinery, Inc, 2019. p. 49-50 (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - With the rapid development of Software-Defined Networking (SDN) advocating a centralized view of networks, efficient and reliable Distributed Denial of Service (DDoS) defenses are necessary to protect the centralized SDN controller. In this work, we explore the robustness of DL-based DDoS defenses in SDN against adversarial learning attacks. First, we investigate generic off-the-shelf adversarial attacks to test the robustness of DDoS defenses in SDN. Then, we propose Flow-Merge for realistic adversarial flows while achieving a high evasion rate. The evaluation shows that the proposed Flow-Merge is able to force the DL-based DDoS defenses to misclassify 100% of benign flows as malicious.

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Abusnaina A, Yuksel M, Nyang DH, Mohaisen A. Examining the security of DDoS detection systems in software defined networks. In CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019. Association for Computing Machinery, Inc. 2019. p. 49-50. (CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019). doi: 10.1145/3360468.3368174

Examining the security of DDoS detection systems in software defined networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this