BibTex RIS Kaynak Göster
Yıl 2015, Cilt: 36 Sayı: 3, 2686 - 2692, 13.05.2015

Öz

Kaynakça

  • D. Fisch, A. Hofmann and B. Sick, " On the versatility of radial basis function neural networks: A case study in the field of intrusion detection", Information Sciences, Volume 180, Issue 12, pp. 2421-2439, 2010.
  • Wang, G., et al., "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering". Expert Systems with Applications, 2010. 37(9): p. 6225-6232.
  • Li, Y., et al., "Network anomaly detection based on TCM-KNN algorithm, in Proceedings of the 2nd ACM symposium on Information", computer and communications security. 2007, ACM: Singapore. p. 13-19.
  • Muna Mhammad T. Jawhar. Et al.," Design Network Intrusion Detection System using hybrid Fuzzy-Neural Network". Computer Science and Engineering, 2011. V (4): Issue (3).
  • Horng, S.-J., et al., "A novel intrusion detection system based on hierarchical clustering and support vector machines". Expert Systems with Applications, 2011. 38(1): p. 306-313.
  • M. Sheikhan and M. Sharifi Rad, "Misuse detection based on feature selection by fuzzy association rule mining", World Applied Sciences Journal, 10 (Special Issue of Computer & Electrical Engineering), pp. 32-40, 2010.
  • Pietraszek, T. and A. Tanner, Data mining and machine learning-Towards reducing false positives in intrusion detection. Inf. Secur. Tech. Rep., 2005. 10(3): p. 169-183.
  • Sangkatsanee, P., N. Wattanapongsakorn, and C. Charnsripinyo, "Practical real-time intrusion detection using machine learning approaches". Computer Communications, 2011. 34(18): p. 2227-2235.
  • Yun Wang, "A multinomial logistic regression modeling approach for anomaly intrusion detection", Computers & Security, Volume 24, Issue 8, November 2005, P662-674.
  • Zuev, D. and A. Moore . Traffic Classification Using a Statistical Approach.Passive and Active Network Measurement. C. Dovrolis, Springer Berlin Heidelberg.,2005 . 3431: p. 321-324.
  • Wang, G., et al., "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering". Expert Systems with Applications, 2010. 37(9): p. 6225-6232.
  • Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Boston: Morgan Kaufmann Publishers.
  • A. Z. Al-Garni, A. Jamal, A. M. Ahmad.2006. "Neural network-based failure rate prediction for De Havilland Dashtires". Journal of Engineering Applications of Artificial Intelligence 19 681-691.

A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection

Yıl 2015, Cilt: 36 Sayı: 3, 2686 - 2692, 13.05.2015

Öz

Abstract. Increased intrusions into computer networks and cyber-attacks have rendered the immunization of cyberspace one of the most important issues of managers and experts in the recent years. Since cyber-attacks have become more sophisticated and hackers have become more professional, mere use techniques such as firewall, cryptography, biometrics, and antiviruses is not sufficient anymore. Therefore, it is necessary to employ efficient intrusion detection systems. Considering 5 classes of cyber-attacks, a detection intrusion system, of abuse detection type, based on the combination of a multilayer artificial neural network and an intelligent feature selection method was introduced in this research. The research results indicated that the feature selection phase using the proposed method yielded more favorable outcomes than the compared method in terms of the evaluation criteria.

Kaynakça

  • D. Fisch, A. Hofmann and B. Sick, " On the versatility of radial basis function neural networks: A case study in the field of intrusion detection", Information Sciences, Volume 180, Issue 12, pp. 2421-2439, 2010.
  • Wang, G., et al., "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering". Expert Systems with Applications, 2010. 37(9): p. 6225-6232.
  • Li, Y., et al., "Network anomaly detection based on TCM-KNN algorithm, in Proceedings of the 2nd ACM symposium on Information", computer and communications security. 2007, ACM: Singapore. p. 13-19.
  • Muna Mhammad T. Jawhar. Et al.," Design Network Intrusion Detection System using hybrid Fuzzy-Neural Network". Computer Science and Engineering, 2011. V (4): Issue (3).
  • Horng, S.-J., et al., "A novel intrusion detection system based on hierarchical clustering and support vector machines". Expert Systems with Applications, 2011. 38(1): p. 306-313.
  • M. Sheikhan and M. Sharifi Rad, "Misuse detection based on feature selection by fuzzy association rule mining", World Applied Sciences Journal, 10 (Special Issue of Computer & Electrical Engineering), pp. 32-40, 2010.
  • Pietraszek, T. and A. Tanner, Data mining and machine learning-Towards reducing false positives in intrusion detection. Inf. Secur. Tech. Rep., 2005. 10(3): p. 169-183.
  • Sangkatsanee, P., N. Wattanapongsakorn, and C. Charnsripinyo, "Practical real-time intrusion detection using machine learning approaches". Computer Communications, 2011. 34(18): p. 2227-2235.
  • Yun Wang, "A multinomial logistic regression modeling approach for anomaly intrusion detection", Computers & Security, Volume 24, Issue 8, November 2005, P662-674.
  • Zuev, D. and A. Moore . Traffic Classification Using a Statistical Approach.Passive and Active Network Measurement. C. Dovrolis, Springer Berlin Heidelberg.,2005 . 3431: p. 321-324.
  • Wang, G., et al., "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering". Expert Systems with Applications, 2010. 37(9): p. 6225-6232.
  • Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. Boston: Morgan Kaufmann Publishers.
  • A. Z. Al-Garni, A. Jamal, A. M. Ahmad.2006. "Neural network-based failure rate prediction for De Havilland Dashtires". Journal of Engineering Applications of Artificial Intelligence 19 681-691.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Bölüm Derleme
Yazarlar

Mehdi Mansourı

Mohadese Torabı Golsefıd Bu kişi benim

Naser Nematbakhsh Bu kişi benim

Yayımlanma Tarihi 13 Mayıs 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 36 Sayı: 3

Kaynak Göster

APA Mansourı, M., Torabı Golsefıd, M., & Nematbakhsh, N. (2015). A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 2686-2692.
AMA Mansourı M, Torabı Golsefıd M, Nematbakhsh N. A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. Mayıs 2015;36(3):2686-2692.
Chicago Mansourı, Mehdi, Mohadese Torabı Golsefıd, ve Naser Nematbakhsh. “A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36, sy. 3 (Mayıs 2015): 2686-92.
EndNote Mansourı M, Torabı Golsefıd M, Nematbakhsh N (01 Mayıs 2015) A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36 3 2686–2692.
IEEE M. Mansourı, M. Torabı Golsefıd, ve N. Nematbakhsh, “A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection”, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, c. 36, sy. 3, ss. 2686–2692, 2015.
ISNAD Mansourı, Mehdi vd. “A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36/3 (Mayıs 2015), 2686-2692.
JAMA Mansourı M, Torabı Golsefıd M, Nematbakhsh N. A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36:2686–2692.
MLA Mansourı, Mehdi vd. “A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, c. 36, sy. 3, 2015, ss. 2686-92.
Vancouver Mansourı M, Torabı Golsefıd M, Nematbakhsh N. A Hybrid Intrusion Detection System Based on Multilayer Artificial Neural Network and Intelligent Feature Selection. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36(3):2686-92.