Artificial Neural Network Approaches to Intrusion Detection: A Review

Ahmad I., Abdullah A. B., Alghamdi A. S.

8th WSEAS International Conference on Telecommunications and Informatics, İstanbul, Türkiye, 30 Mayıs - 01 Haziran 2009, ss.200-201 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.200-201
  • Anahtar Kelimeler: Artificial Neural Network, Intrusion Detection System, Anomaly Detection, False positive, Negative, ROC, Detection Rate, RMSE, IDA, MLP
  • Marmara Üniversitesi Adresli: Hayır

Özet

Intrusion detection systems arc the foremost tools for providing safety in computer and network-system. There are many limitations in traditional IDSs like time consuming statistical analysis, regular updating, non adaptive accuracy and flexibility. It is an Artificial Neural Network that supports an ideal specification of an Intrusive Detection System and is a solution to the problems of traditional IDSs. Therefore, An Artificial Neural Network inspired by nervous system has become an interesting tool in the applications of Intrusion Detection Systems due to promising features. Intrusion detection by Artificial Neural Networks is an ongoing area. In this paper, we provide an introduction and review of the Artificial Neural Network Approaches within Intrusion Detection, in addition to m; suggestions for future research. We also discuss on tools and datasets that are being used in Artificial Neural Network Intrusion Detection Systems. This review may help the researcher to develop new optimize approach in the field of Intrusion Detection.