CFP last date
20 January 2025
Reseach Article

Intrusion Detection System for Wireless ADHOC Network using Time Series Techniques

by M. Ashikur Rahman, Sabbir M. Saleh, Syed Maruful Huq
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 1
Year of Publication: 2017
Authors: M. Ashikur Rahman, Sabbir M. Saleh, Syed Maruful Huq
10.5120/ijca2017913408

M. Ashikur Rahman, Sabbir M. Saleh, Syed Maruful Huq . Intrusion Detection System for Wireless ADHOC Network using Time Series Techniques. International Journal of Computer Applications. 162, 1 ( Mar 2017), 38-42. DOI=10.5120/ijca2017913408

@article{ 10.5120/ijca2017913408,
author = { M. Ashikur Rahman, Sabbir M. Saleh, Syed Maruful Huq },
title = { Intrusion Detection System for Wireless ADHOC Network using Time Series Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 1 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number1/27211-2017913408/ },
doi = { 10.5120/ijca2017913408 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:49.059924+05:30
%A M. Ashikur Rahman
%A Sabbir M. Saleh
%A Syed Maruful Huq
%T Intrusion Detection System for Wireless ADHOC Network using Time Series Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 1
%P 38-42
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computer security and intrusion detection has developed into progressively more significant in recent computer sector, which is providing security of confidential data and information. At present, different progress and advances of intrusion detection is applying and operating, although in consequence, these progressions are comparatively unsuccessful and ineffective. Latest resources and approaches will reduce these limitations. This thesis document is going to proposed a positive and vibrant analysis, concerning on trend analysis which will be effective to decrease and deal with intrusion in ADHOC network. In the ground of intrusion detection, research has been ongoing since about 20 years. Intrusion detection systems appear a second line of defense that recognizes a report attack in real time. Modern world provides the latest system of internet which is disputing for the security of information systems. For the lack of domain familiarity, Intrusion Detection system can fall squat to recognize new attack. To cope with latest attack, database should be rationalized time to time. Possibility of vulnerability to attacks increases for their flexible nature. A few intrusion detection systems which are used for wired network, those are not sufficient for Wireless and ADHOC networks. In ADHOC networks, it is significant for such slant that is proficient to intellect any variety of eccentric actions. In fact, it is out of ability of technology to detect each single contravention. In this research we are going to model an Intrusion Detection System using time series techniques for wireless ADHOC network by which it can detect intrusion. Time series is a technique which can analyze data. Then we will use an unsupervised learning method clustering, to detect intrusion.

References
  1. Azer M, El-Kassas S, Hassan AW, El-Soudani M. Intrusion Detection for Wormhole Attacks in ADHOC Networks: A Survey and a Proposed Decentralized Scheme. InAvailability, Reliability and Security, 2008. ARES 08. Third International Conference on 2008 Mar 4 (pp. 636-641)
  2. Píštěk M. Zabezpečení podnikové sítě ve společnosti INPOST, spol. s ro, Uherské Hradiště.
  3. Meng L, Dipoala WS, Grimm WM, inventors; Robert Bosch GmbH, assignee. Dual sensing intrusion detection method and system with state-level fusion. United States patent US 7,262,697. 2007
  4. Wu Q, Shao Z. Network anomaly detection using time series analysis. InJoint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services-(icas-isns' 05) 2005 Oct 23 (pp. 42-42)
  5. Inam ul haq, 2009, Intrusion detection using K means algorithm in Wireless ADHOC Network, University of Hertfordshire
  6. Amitabh Mishra, KetanNadkarni and Animeshpatcha, Intrusion Detection in Wireless ADHOC networks, February 2004
  7. James Douglas Hamilton, 1994 , Time series analysis, Page 25
  8. Rizwan Qayyum,2006, Security in ADHOC Networks, University of Hertfordshire pp 14-18
  9. YanetManzano, Tracing the Development of Denial of Service Attacks: A Corporate Analogy
  10. Stephen Northcutt ,Network intrusion detection, Third edition, page 271
  11. XuanLongNguyen, 2006, Anomaly and sequential detection with time series data, Y Zhang, Intrusion Detection in Wireless ADHOC Network
  12. Marcov Carvolho(2008), Security in mobile ADHOC network.
  13. Rizwan Qayyum,2006, Security in ADHOC Networks, , University of Hertfordshire pp 14-18
  14. XuanLongNguyen, 2006, Anomaly and sequential detection with time series data, Y Zhang, Intrusion Detection in Wireless ADHOC Network
  15. ÖzleyişOcakoğlu,, A Probabilistic Routing Disruption Attack on DSR and Its Analysis
  16. Anderberg, M. R. 1973 Cluster Analysis for Applications. Academic Press, New York, NY.
  17. John Heideman, 2002 ,IPAM tutorial: Network modeling and traffic analysis with ns-2”, presentation at the UCLA/Institute for Pure and Applied Mathematics, Los Angeles, USA
  18. A Mitrokotsa, 2006, Intrusion Detection of Packet Dropping Attacks inMobile ADHOC Networks
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection System IDS Wireless ADHOC Network Time Series