You are not logged in.

Real time cyber attack analysis on Hadoop ecosystem using machine learning algorithms

Khorshed, Md Tanzim, Sharma, Neeraj A., Dutt, Aaron V., Ali, A. B. M. Shawat and Xiang, Yang 2015, Real time cyber attack analysis on Hadoop ecosystem using machine learning algorithms, in APWC on CSE 2015: Proceedings of the Asia-Pacific Computer Science and Engineering 2015 World Congress, IEEE, Piscataway, N.J., pp. 1-7, doi: 10.1109/APWCCSE.2015.7476223.

Attached Files
Name Description MIMEType Size Downloads

Title Real time cyber attack analysis on Hadoop ecosystem using machine learning algorithms
Author(s) Khorshed, Md Tanzim
Sharma, Neeraj A.
Dutt, Aaron V.
Ali, A. B. M. Shawat
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Conference name Asia-Pacific Computer Science and Engineering. World Congress (2nd : 2015 : Nadi, Fiji)
Conference location Nadi, Fiji
Conference dates 2-4 Dec. 2015
Title of proceedings APWC on CSE 2015: Proceedings of the Asia-Pacific Computer Science and Engineering 2015 World Congress
Editor(s) [Unknown]
Publication date 2015
Conference series Asia-Pacific Computer Science and Engineering World Congress
Start page 1
End page 7
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) big data
Hadoop
Ambari
internet of things
classification
machine learning
cloud computing
cyber-attack
Summary Big Data technologies are exciting cutting-edge technologies that generate, collect, store and analyse tremendous amount of data. Like any other IT revolution, Big Data technologies also have big challenges that are obstructing it to be adopted by wider community or perhaps impeding to extract value from Big Data with pace and accuracy it is promising. In this paper we first offer an alternative view of «Big Data Cloud» with the main aim to make this complex technology easy to understand for new researchers and identify gaps efficiently. In our lab experiment, we have successfully implemented cyber-attacks on Apache Hadoop's management interface «Ambari». On our thought about «attackers only need one way in», we have attacked the Apache Hadoop's management interface, successfully turned down all communication between Ambari and Hadoop's ecosystem and collected performance data from Ambari Virtual Machine (VM) and Big Data Cloud hypervisor. We have also detected these cyber-attacks with 94.0187% accurateness using modern machine learning algorithms. From the existing researchs, no one has ever attempted similar experimentation in detection of cyber-attacks on Hadoop using performance data.
ISBN 9781509007141
Language eng
DOI 10.1109/APWCCSE.2015.7476223
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084608

Document type: Conference Paper
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 18 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Fri, 01 Jul 2016, 16:18:11 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.