Analyzing feasibility for deploying very fast decision tree for DDoS attack detection in cloud-assisted WBAN

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. © 2014 Springer International Publishing Switzerland.