You are here

Home » Content

A New Approach For Feature Selection In Intrusion Detection System

Journal Name:

  • Cumhuriyet Üniversitesi Fen Fakültesi Fen Bilimleri Dergisi

Publication Year:

  • 2015

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
There are numerous intrusion detection systems with different methods for detection of the attacks whose main challenge is enhancement of the efficiency and accuracy rates. Therefore, development of methods to enhance their efficiency is necessary. In this paper, we are searching for a solution to reduce number of the features. Cuttlefish algorithm was used to investigate all of the features and numbers including 3, 5, 10, and 13 were used for the analysis. Artificial neural network was used as the evaluation function and the results of this paper were compared with other papers. Among the selected features, the number thirteen feature had the highest efficiency and could detect nearly all of the attacks.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon 5000146245-5000241985-1-pb.pdf
  • 6
1344
1357
  • English

REFERENCES

References: 

[1] S. Wu, , and Y. Ester, "Data mining-based intrusion detectors." Expert Systems with
Applications 36.3 , PP 5605-5612, 2009.
[2] H. Liu et al., “Boosting feature selection using information metric for classification”,
Neurocomputing, Vol. 73, No. 1, pp. 295 –303, 2009.
[3] L. Yu, H. Liu, “Efficient feature selection via analysis of relevance and redundancy”, Journal of
Machine Learning Research, Vol.5, No. 2, pp. 1205 – 1224, 2004.
[4] H. Liu, L. Yu, “toward integrating feature selection algorithms for classification and clustering”,
IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 4, pp. 491 – 502, 2005.
[5] Amrita and P. Ahmed, "A Hybrid-Based Feature Selection Approach For IDS", Springer
International Publishing Switzerland,2014;
[6] Adel Sabry Eesa, Zeynep Orman, Adnan Mohsin Abdulazeez Brifcani," novel feature-selection
approach based on the cuttlefish optimization algorithm for intrusion detection systems", Expert
Systems with Applications 42, 2670–2679,2015.
[7] Le Thi, Hoai An, et al. "A Filter Based Feature Selection Approach in MSVM Using DCA and Its
Application in Network Intrusion Detection." Intelligent Information and Database Systems.
Springer International Publishing, 2014. 403-413.
[8] http://fa.swewe.net/word_show.htm

Thank you for copying data from http://www.arastirmax.com