Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Enhancing the Performance of Network Intrusion Detection System by Combining Naïve Bayes, Decision Tree and K-Nearest Neighbors Algorithms

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Abeselom Befekadu
10.5120/ijca2018917351

Abeselom Befekadu. Enhancing the Performance of Network Intrusion Detection System by Combining Naïve Bayes, Decision Tree and K-Nearest Neighbors Algorithms. International Journal of Computer Applications 180(49):48-53, June 2018. BibTeX

@article{10.5120/ijca2018917351,
	author = {Abeselom Befekadu},
	title = {Enhancing the Performance of Network Intrusion Detection System by Combining Naïve Bayes, Decision Tree and K-Nearest Neighbors Algorithms},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2018},
	volume = {180},
	number = {49},
	month = {Jun},
	year = {2018},
	issn = {0975-8887},
	pages = {48-53},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume180/number49/29573-2018917351},
	doi = {10.5120/ijca2018917351},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Protecting the hostile network environment is a very difficult task. Although, there is no way to protect the network for hundred percent accuracy, so many researches tried to achieve the best security mechanisms for long time. Among the security mechanisms, network intrusion detection system is one of the well-known. The performances of the network intrusion detection systems that are developed have produce so many false alarm. To improve this false alarm rate this research combines three algorisms which are Naïve Bayes, Decision Tree and k-NN. The results found from the experiment showed that the combined algorithm improve the accuracy of the network intrusion detection system by up to 5%.

References

  1. C. A. K. Sumaiya Thaseen, "analysis of supervised Tree based classifiers for intrusion detection system," International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013.
  2. R. K. C. Nidhi Srivastav, "Novel Intrusion Detection System integrating Layered Framework with Neural Network," IEEE, 2012.
  3. M. P. a. M. R. Patra, "A Comparative Study Of Data Mining Algorithms For," IEEE, pp. 504-507, 2008.
  4. Q. Y. D. R. Juan Wang, "An intrusion detection algorithm based on decision tree technology," IEEE, 2009.
  5. Y. Freund, "The Alternating Decision Tree Algorithm," ICML, pp. 124-133.
  6. D. Tigabu, "Constructing Predictive Model for Network Intrusion Detection," 2012.
  7. S. B. a. Z. E. N. Ben Amor, "Naive Bayesian Networks in Intrusion Detection Systems," 2000.

Keywords

Network Intrusion, Network Security, Intrusion Detection System