• Evgeniya Nikolova
  • Veselina Jecheva
Ключови думи: детекция на инструкции, аномалии, класификационно дърво относителна ентропия


In recent years anomaly detection has become an important area for both commercial interests as well as academic research. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. The present paper proposed an adaptive approach of anomaly based intrusion detection which is grounded on classification trees and relative entropy. The major results of the implemented simulation experiments are presented and discussed as well.


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Как да се цитира
Nikolova, E., & Jecheva, V. (2018). КЛАСИФИКАЦИОННО ДЪРВО И KULLBACK-LEIBLER РАСТОЯНИЕ-ОСНОВАНО НА ДЕТЕКТИРАНЕ НА АНОМАЛНИ ИНСТРУКЦИИ. КОМПЮТЪРНИ НАУКИ И КОМУНИКАЦИИ, 3(1), 40-44. изтеглен на от https://csc.bfu.bg/index.php/CSC/article/view/153
Компютърни науки и комуникации - рецензирани публикации. ISSN: 1314-7846

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