Detection Of Distributed Denial of Service Attack With Hadoop On Live Network
Suchita Korad, Madhuri Jadhav, Shubhada Kadam, Prajakta Deore , Prof.Rahul Patil
Hadoop, HDFS, DDoS, Flooding attacks
Distributed Denial of Service i.e. DDoS flooding attacks are one of the biggest challenges to the availability of online services now-a-days. These DDoS attacks overwhelm the victim with huge volume of traffic and render it incapable of performing communication or crashes it completely. If there are delays in detecting the flooding attacks, we have to manually disconnect the victim and fix the problem. With the rapid increase of DDoS attack volume and frequency, the current DDoS detection techniques are challenged to deal with huge attack volume in reasonable and affordable response time.In this paper, we had proposed, a Hadoop based Live DDoS Detection framework to tackle efficient analysis of flooding attacks by using core components of Hadoop like MapReduce and HDFS. We implemented a counter-based DDoS detection algorithm for four major flooding attacks (TCP-SYN, UDP and ICMP, HTTP GET) in MapReduce, consisting of mapper and reducer functions. We deployed a testbed to evaluate the performance of Hadoop framework for live DDoS detection. Based on the experiment we showed that Hadoop is capable of processing and detecting DDoS attacks in affordable time.
Article Details
Unique Paper ID: 143688

Publication Volume & Issue: Volume 3, Issue 1

Page(s): 269 - 272
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