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A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

Ruomeng Xu,Jieren Cheng,Fengkai Wang,Xiangyan Tang,Jinying Xu

Published 2018 in International Conference on Algorithms and Architectures for Parallel Processing

ABSTRACT

Distributed Denial of Service (DDoS) has developed multiple variants, one of which is Distributed Reflective Denial of Service (DRDoS). With the increasing number of Internet of Things (IoT) devices, the threat of DRDoS attack is growing, and the damage of a DRDoS attack is more destructive than other types. The existing DDoS detection methods cannot be generalized in DRDoS early detection, which leads to heavy load or degradation of service when deployed at the final point. In this paper, we propose a DRDoS detection and defense method based on deep forest model (DDDF), and then we integrate differentiated service into defense model to filter out DRDoS attack flow. Firstly, from the statistics perspective on different stages of DRDoS attack flow in the big data environment, we extract a host-based DRDoS threat index (HDTI) from the network flows. Secondly, using the HDTI feature we build a DRDoS detection and defense model based on the deep forest, which consists of 1 extreme gradient boost (XGBoost) forest estimator, 2 random forest estimators, and 2 extra random forest estimators in each layer. Lastly, the differentiated service procedure applies the detection result from DDDF to drop the traffic identified in different stages and different detection points. Theoretical analysis and experiments show that the method we proposed can effectively identify DRDoS attack with higher detection rate and a lower false alarm rate, the defense model also shows distinguishing ability to effectively eliminate the DRDoS attack flows, and dramatically mitigate the damage of a DRDoS attack.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    International Conference on Algorithms and Architectures for Parallel Processing

  • Publication date

    2018-11-15

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1007/978-3-030-05234-8_21
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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