title: Ð¡Ð¸ÑÑÐµÐ¼Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐÐÐ¡ (ÐÐÐÐ¡) Ð°ÑÐ°Ðº
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Intrusion Detection System using recurrent neural network to detect DOS (DDOS) attacks
Creators: name: Ð¡Ð°Ð»Ð°Ð³Ð°ÐµÐ², ÐÑÑÐµÐ¼
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2019
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÐµ Ð¼Ð°ÑÐ¸Ð½Ñ ÑÐ»ÐµÐºÑÑÐ¾Ð½Ð½ÑÐµ Ð¿ÐµÑÑÐ¾Ð½Ð°Ð»ÑÐ½ÑÐµ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐ¸ÑÑÐµÐ¼Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ñ
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ

description: Ð Ð°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½ÑÐµ Ð°ÑÐ°ÐºÐ¸ Ð½Ð° ÑÐµÑÐ²ÐµÑÐ° ÑÐ¸Ð¿Ð° Â«Ð¾ÑÐºÐ°Ð· Ð² Ð¾Ð±ÑÐ»ÑÐ¶Ð¸Ð²Ð°Ð½Ð¸Ð¸Â» (DDoS) - ÑÑÐ¾ ÑÐ°Ð¼ÑÐ¹ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÑÐ½Ð½ÑÑ ÑÐ¸Ð¿Ð¾Ð² Ð°ÑÐ°Ðº Ð² Ð¸Ð½ÑÐµÑÐ½ÐµÑÐµ. Ð­ÑÐ¾ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð»ÐµÐ³ÐºÐ¾ Ð·Ð°Ð¿ÑÑÐºÐ°ÐµÐ¼ÑÐµ Ð°ÑÐ°ÐºÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ Ð¾ÑÐµÐ½Ñ ÑÐ»Ð¾Ð¶Ð½Ð¾ Ð¸ Ð´Ð¾ÑÐ¾Ð³Ð¾ Ð¾Ð±Ð½Ð°ÑÑÐ¶Ð¸ÑÑ Ð¸ ÑÑÑÑÐ°Ð½Ð¸ÑÑ. ÐÐ²Ð¸Ð´Ñ ÑÐ°Ð·ÑÑÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ ÑÑÑÐµÐºÑÐ° DDoS Ð°ÑÐ°Ðº - Ð²Ð¾Ð·ÑÐ¾ÑÐ»Ð° Ð·Ð½Ð°ÑÐ¸Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ DDoS Ð°ÑÐ°Ðº ÑÑÐµÐ´Ð¸ Ð¾Ð³ÑÐ¾Ð¼Ð½Ð¾Ð³Ð¾ Ð¿Ð¾ÑÐ¾ÐºÐ° ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ°. ÐÑÑÐ¾ÑÐ¸Ñ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÐµÑ, ÑÑÐ¾ ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ DDoS Ð°ÑÐ°Ðº, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° ÑÐ¸Ð³Ð½Ð°ÑÑÑÐ½Ð¾Ð¼ Ð°Ð½Ð°Ð»Ð¸Ð·Ðµ, Ð¿Ð¾Ð»ÐµÐ·Ð½Ñ, Ð¾Ð´Ð½Ð°ÐºÐ¾, Ð¾Ð³ÑÐ¾Ð¼Ð½ÑÐ¹ Ð¿Ð¾ÑÐ¾Ðº Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð±Ð¾Ð»ÑÑÐ°Ñ Ð²Ð°ÑÐ¸Ð°ÑÐ¸Ð²Ð½Ð¾ÑÑÑ, Ð·Ð°ÑÑÑÐ´Ð½ÑÐµÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°ÐºÐ¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð°. Ð¡Ð»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾, ÑÑÐ° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ°Ñ ÑÐ°Ð±Ð¾ÑÐ° ÑÐ¾ÑÑÐµÐ´Ð¾ÑÐ¾ÑÐµÐ½Ð° Ð½Ð° Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÑ Ð½Ð° Ð¼ÐµÑÐ¾Ð´Ð¸ÐºÐµ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð­ÑÐ¾ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¾ÐºÑÑÐ¸ÑÑÐµÑÑÑ Ð½Ð° Ð¾Ð´Ð½Ð¾Ð¼ Ð¸Ð· ÑÐ¸Ð¿Ð¾Ð² ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð½Ð°Ð·ÑÐ²Ð°ÐµÐ¼Ð¾Ð¹ Long Short-Term Memory (LSTM) Ð¸ Ð¸Ð½ÑÑÐ°ÑÑÑÑÐºÑÑÑÐµ Keras, TensorFlow, Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸. Ð­ÑÐ° Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð° Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ð° ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐ¾ÑÐ¶ÐµÐ½Ð¸Ð¹ (IDS), ÑÑÐ¾Ð±Ñ Ð¿Ð¾Ð¼Ð¾ÑÑ Ð² Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ð¸ DDoS Ð°ÑÐ°Ðº, Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÑ Ð²ÑÐ¾Ð´ÑÑÐ¸Ð¹ ÑÑÐ°ÑÐ¸Ðº ÑÐµÑÐ¸. ÐÑÐ¾Ð¼Ðµ ÑÐ¾Ð³Ð¾, Ð¾Ð¶Ð¸Ð´Ð°ÐµÑÑÑ, ÑÑÐ¾ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð±ÑÐ´ÐµÑ Ð¸Ð¼ÐµÑÑ Ð±Ð¾Ð»ÐµÐµ Ð²ÑÑÐ¾ÐºÐ¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ Ð½Ð¸Ð·ÐºÐ¸Ð¹ ÑÑÐ¾Ð²ÐµÐ½Ñ Ð»Ð¾Ð¶Ð½ÑÑ ÑÑÐ°Ð±Ð°ÑÑÐ²Ð°Ð½Ð¸Ð¹, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ IDS Ð¿ÑÐ¾Ð¹Ð´ÑÑ ÑÑÐ¿ÐµÑÐ½ÑÐµ ÑÐµÑÑÐ¾Ð²ÑÐµ Ð¸ÑÐ¿ÑÑÐ°Ð½Ð¸Ñ.
description: Distributed denial - of-service (DDoS) attacks are the most common types of attacks on the Internet. These are the most easily launched attacks, which are very difficult and expensive to detect and eliminate. Due to the destructive effect of DDoS attacks-increased importance of methods of detection of DDoS attacks among the huge flow of network traffic. History shows that traditional methods of DDoS attack detection based on signature analysis are useful, however, the huge data flow and large variability makes it difficult to use this approach. Consequently, this research work focuses on the use of detection techniques based on deep learning techniques. This study focuses on one type of recurrent neural network called Long Short-Term Memory (LSTM) and the Keras infrastructure, TensorFlow, to build and train the model. This model is used in the development of a prototype intrusion detection system (IDS) to assist in the detection of DDoS attacks by analyzing incoming network traffic. In addition, the resulting model is expected to have higher detection accuracy and low false positives, and the IDs prototype will pass successful test trials.