Intrusion Detection with Machine Learning Techniques: Comparative Analysis

Volume: 26 Number: 3 March 16, 2015
  • Çetin Kaya
  • Oktay Yıldız
EN TR

Intrusion Detection with Machine Learning Techniques: Comparative Analysis

Abstract

The Internet is an indispensable part of our daily lives. The increasing number of web applications and the user, in terms of data security, has some risks. Intrusion detection systems, secure access to internal networks to detect attacks and unexpected due to the demands of one of the important tools for network security. In order to develop more effective intrusion detection systems a lot of investigative work. However, so many different machine learning techniques in the literature with intrusion-detection system. In this study, the intrusion detection systems are frequently used in machine learning techniques are researched, evaluated, and the resulting achievements classifiers, used by datasets. To this end between the years 2007-2013 65 article examined, the results are presented in a way that the comparative. Thus, the determination of the future machine learning techniques to gain a perspective on the work of the attack.

Keywords

References

  1. «ICT Statistics Home Page» [Çevrimiçi],
  2. http://http://www.itu.int/en/ITU
  3. D/Statistics/Documents/facts/ICTFactsFigures2013-e.pdf.
  4. erişilmiştir]. [30 04 2014 tarihinde
  5. X. Zhang, L. Jia, H. Shi, Z. Tang ve X. Wang, «The Application of Machine Learning Methods to Intrusion Detection,» 2012.
  6. J. Co, Computer Security Threat Monitoring and Surveillance, Pennsylvania: James P. Anderson Company, Fort Washington, 1980.
  7. R. Bace ve P. Mell, «NIST Special Publication on Intrusion Detection Systems,» Publications of National Institute of Standards and Technology, pp. 1-53, 2011.
  8. Y. Vural ve Ş. Sağıroğlu, «Kurumsal Bilgi Güvenliğinde Güvenlik Testleri ve Öneriler,» Gazi Üniv. Müh. Mim. Fak. Der., cilt 26, no. 1, pp. 89-103, 2011.

Details

Primary Language

English

Subjects

-

Journal Section

-

Authors

Çetin Kaya This is me

Oktay Yıldız This is me

Publication Date

March 16, 2015

Submission Date

December 12, 2014

Acceptance Date

-

Published in Issue

Year 2014 Volume: 26 Number: 3

APA
Kaya, Ç., & Yıldız, O. (2015). Intrusion Detection with Machine Learning Techniques: Comparative Analysis. Marmara Fen Bilimleri Dergisi, 26(3), 89-104. https://doi.org/10.7240/mufbed.24684
AMA
1.Kaya Ç, Yıldız O. Intrusion Detection with Machine Learning Techniques: Comparative Analysis. MAJPAS. 2015;26(3):89-104. doi:10.7240/mufbed.24684
Chicago
Kaya, Çetin, and Oktay Yıldız. 2015. “Intrusion Detection With Machine Learning Techniques: Comparative Analysis”. Marmara Fen Bilimleri Dergisi 26 (3): 89-104. https://doi.org/10.7240/mufbed.24684.
EndNote
Kaya Ç, Yıldız O (March 1, 2015) Intrusion Detection with Machine Learning Techniques: Comparative Analysis. Marmara Fen Bilimleri Dergisi 26 3 89–104.
IEEE
[1]Ç. Kaya and O. Yıldız, “Intrusion Detection with Machine Learning Techniques: Comparative Analysis”, MAJPAS, vol. 26, no. 3, pp. 89–104, Mar. 2015, doi: 10.7240/mufbed.24684.
ISNAD
Kaya, Çetin - Yıldız, Oktay. “Intrusion Detection With Machine Learning Techniques: Comparative Analysis”. Marmara Fen Bilimleri Dergisi 26/3 (March 1, 2015): 89-104. https://doi.org/10.7240/mufbed.24684.
JAMA
1.Kaya Ç, Yıldız O. Intrusion Detection with Machine Learning Techniques: Comparative Analysis. MAJPAS. 2015;26:89–104.
MLA
Kaya, Çetin, and Oktay Yıldız. “Intrusion Detection With Machine Learning Techniques: Comparative Analysis”. Marmara Fen Bilimleri Dergisi, vol. 26, no. 3, Mar. 2015, pp. 89-104, doi:10.7240/mufbed.24684.
Vancouver
1.Çetin Kaya, Oktay Yıldız. Intrusion Detection with Machine Learning Techniques: Comparative Analysis. MAJPAS. 2015 Mar. 1;26(3):89-104. doi:10.7240/mufbed.24684

Cited By

Marmara Journal of Pure and Applied Sciences

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