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Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

机译：对抗SDN环境中DDOS攻击的对抗深度学习方法检测与防御

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摘要

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.
机译：在过去的几年中，软件定义的网络（SDN）范例已成为设计未来网络的新兴架构，并满足新的应用需求。 SDN提供通过分离控制和数据平面来改善网络控制和管理的资源，并在控制器中集中逻辑控制。但是，集中控制逻辑可以是恶意攻击的理想目标，主要是分布式拒绝服务（DDOS）攻击。最近，深入学习已成为应用于网络安全的强大技术，最近的研究中提出了许多网络入侵检测（NID）。一些研究表明，深度神经网络在检测对抗攻击方面是敏感的。对抗性攻击是具有某些扰动的情况，导致深度神经网络错误分类。在本文中，我们提出了一种基于SDN的对抗性训练的检测和防御系统，它利用生成的对抗网络（GAN）框架来检测DDOS攻击并应用对抗性训练，使系统对对抗性攻击不太敏感。该建议的系统包括明确定义的模块，可以使用IP流量分析实现连续流量监控，使异常检测系统能够在近实时起作用。我们对两个不同情景进行了实验，具有模拟数据和2019年公共数据集CICDDOS。实验结果表明，与其他方法相比，该系统有效地检测到最新的常见类型的DDOS攻击。

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  • 来源
    《Future generation computer systems》 |2021年第12期|156-167|共12页
  • 作者

    Matheus P. Novaes; Luiz F. Carvalho; Jaime Lloret; Mario Lemes Proenca Jr;

  • 作者单位

    Electric Engineering Department State University of Londrina (UEL) Londrina Parana Brazil;

    Computer Engineering Department Federal Technology University of Parana (UTFPR) Apucarana Parana Brazil;

    Integrated Management Coastal Research Institute Universitat Politecnica de Valencia Valencia Spain;

    Computer Science Department State University of Londrina (UEL) Londrina Parana Brazil;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Adversarial attacks; DDoS; Deep Learning; GAN; SDN;

    机译：对抗性攻击;DDOS;深度学习;甘;SDN.;

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