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An Experience Report on Scalable Implementation of DDoS Attack Detection

  • Sri Yogesh Dorbala
  • Kishore R.
  • Neminath Hubballi
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 215)

Abstract

Distributed Denial of Service (DDoS) attacks are increasingly becoming powerful and crippling many networks and services in Internet. Many methods have been proposed to mitigate and detect DDoS attacks in the literature. These techniques require processing large amount of network traffic in real time. In order to process this bulky network traffic, in this paper we report an experimental investigation of scalable implementation. In our experiments we used distributed computing framework of Apache Hadoop to achieve the scalability. We implemented clustering and classification algorithms for detecting DDoS attack. Several experiments on a DDoS dataset and normal dataset of sizes ranging from 1 GB to 80 GB resulted in performance improvements.

Keywords

Distributed Denial of Service Scalable implementation Attack detection 

Notes

Acknowledgement

The authors would like to acknowledge Center for Applied Internet Data Analysis (CAIDA) and MIT Lincoln Laboratory for providing access to their 2007 DDoS attack dataset and DARPA 99 dataset respectively.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sri Yogesh Dorbala
    • 1
  • Kishore R.
    • 1
  • Neminath Hubballi
    • 1
  1. 1.Discipline of Computer Science and EngineeringIndian Institute of Technology IndoreIndoreIndia

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