Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Result Assessment to Intrusion Detection System using Factors Analysis in MANET

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Poonam Choubey, Rupali Bhartiya
10.5120/ijca2016911758

Poonam Choubey and Rupali Bhartiya. Result Assessment to Intrusion Detection System using Factors Analysis in MANET. International Journal of Computer Applications 152(5):20-25, October 2016. BibTeX

@article{10.5120/ijca2016911758,
	author = {Poonam Choubey and Rupali Bhartiya},
	title = {Result Assessment to Intrusion Detection System using Factors Analysis in MANET},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2016},
	volume = {152},
	number = {5},
	month = {Oct},
	year = {2016},
	issn = {0975-8887},
	pages = {20-25},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume152/number5/26315-2016911758},
	doi = {10.5120/ijca2016911758},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The idea of MANET is basically definite quality because of its unique courses of action and gets the chance to take part. Among different structures which are used in remote methods, flexible ad hoc system is seen as a potential region of work. This system is managed by own resources itself, along these nodes the behavior made for supporting this environment is besides light weighted. When this is a basic functionality has been arrangements which give a basic zone for finding attacker to control the working of the structure and shows effective conduct to avoid interruptions. Over the period of time, particular techniques had been proposed to update the energy issues of recognizing use in MANET. The main idea is to assess effective transmission and each one of the objectives is to make the system full proof which controls the conditions now. Those various issues which highlight the causes of intruder’s, missing node and packet dropping all these issues are resolved from the existing methodology. So, this work gives new parameters for more precision in IDS. Fundamentally these works give more right and corrected measure by utilizing the effective use of information for node and improvement in PDR and Throughputs. By the above qualities the reliability in the system will be improved and effective system will be formed. By this packet, drops can be minimized and intruders can be recognized effectively and prove the high performance.

References

  1. Anant R. More, Vikas N. Nandgaonkar, Dr.ManojNagmode, Pramod P. Patil “ID3 Algorithm for Intrusion Detection” International Conference on Recent Trends in engineering & Technology -2013(ICRTET'2013).
  2. Ahmed Youssef and Ahmed Emam “Network Intrusion Detection using Data Mining and Network.Behavior Analysis” International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 6, Dec 2011.
  3. AnazidaZainal, MohdAizainiMaarof and SitiMariyamShamsuddin “Data Reduction and Ensemble Classifiers in Intrusion Detection” in 2008 IEEE.
  4. Chau M., Xu J.J. and Chen H. (2002) National Conference on Digital Government Research, 271-275.
  5. Devaraju .S, Ramakrishnan .S “Detection of Accuracy for Intrusion Detection System using Neural Network International Journal of Computer Applications (0975 – 8887) Classifier” International Journal of Emerging Technology and Advanced Engineering( ISSN 2250- 2459 (Online), An ISO 9001:2008 Certified Journal, Volume 3, Special Issue 1, January 2013).
  6. Devendrakailashiya, Dr. R.C. Jain “Improve Intrusion Detection Using Decision Tree with Sampling” in IJCTA | MAY-JUNE 2012.
  7. GuangqunZhai, Chunyan Liu “Research and Improvement on ID3 Algorithm in Intrusion Detection System” in 2010 IEEE.
  8. Jorge Blasco, Agustin Orfila, Arturo Ribagorda “Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming” DOI 10.1109/ARES.2010.53 in IEEE 2010.
  9. Joshi .S.A, VarshaS.Pimprale “Network Intrusion Detection System (NIDS) based on Data Mining”International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 1, January 2013.
  10. Mohd. JunedulHaque, Khalid.W. Magid, Nisar Hundewale “An Intelligent Approach for Intrusion Detection Based on Data Mining Techniques” in 2012 IEEE.
  11. YacineBouzida, Frederic Cuppens “Neural networks vs. decision trees for intrusion detection” in 2011.SIGMOD Rec-ord, 30 (4), 25-34.
  12. YacineBouzida, Frederic Cuppens “Neural networks vs. decision trees for intrusion detection” in 2011. SIGMOD Rec-ord, 30 (4), 25-34. Volume 90 – No 12, March 2014

Keywords

Intrusion Detection System (IDS), Packet Delivery Ratio, Throughput, Routing Overhead