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Analyzing Feasibility for Deploying Very Fast Decision Tree for DDoS Attack Detection in Cloud-Assisted WBAN

  • Rabia Latif
  • Haider Abbas
  • Saïd Assar
  • Seemab Latif
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8588)

Abstract

In cloud-assisted wireless body area networks (WBAN), the data gathered by sensor nodes are delivered to a gateway node that collects and aggregates data and transfer it to cloud storage; making it vulnerable to numerous security attacks. Among these, Distributed Denial of Service (DDoS) attack could be considered as one of the major security threats against cloud-assisted WBAN security. To overcome the effects of DDoS attack in cloud-assisted WBAN environment various techniques have been explored during this research. Among these, data mining classification techniques have proven itself as a valuable tool to identify misbehaving nodes and thus for detecting DDoS attacks. Further classifying data mining techniques, Very Fast Decision Tree (VFDT) is considered as the most promising solution for real-time data mining of high speed and non- stationary data streams gathered from WBAN sensors and therefore is selected, studied and explored for efficiently analyzing and detecting DDoS attack in cloud-assisted WBAN environment.

Keywords

Cloud-assisted WBAN Distributed denial of service (DDoS) attack Data mining (DM) Decision trees Very fast decision trees (VFDT) 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rabia Latif
    • 1
  • Haider Abbas
    • 1
    • 2
  • Saïd Assar
    • 3
  • Seemab Latif
    • 1
  1. 1.National University of Sciences and TechnologyIslamabadPakistan
  2. 2.Centre of Excellence in Information Assurance (COEIA)King Saud UniversitySaudi Arabia
  3. 3.Telecom Ecole de Management Information System DepartmentInstitut Mines-TélécomFrance

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