title: ÐÐ°ÑÐ¸ÑÐ° LSTM-ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ° Ð¾Ñ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ 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° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑÐ°
titleType: Subtitle
title: Defence of LSTM classifier of network traffic against adversarial attacks
Creators: name: ÐÑÐ»Ð¸ÐºÐ¾Ð², ÐÐ¼Ð¸ÑÑÐ¸Ð¹
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: 2021
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ ÑÐµÑÐµÐ²ÑÑ Ð°ÑÐ°Ðº
ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð°ÑÐ°ÐºÐ¸
Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ°
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð½Ð° ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð¿ÑÐ¸Ð¼ÐµÑÐ°Ñ
network attack detection
adversarial attacks
poisoning attacks on classifier
adversarial training

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐ¾Ð¹ÐºÐ¾ÑÑÐ¸ LSTM-ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ° Ðº ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐ¼ Ð°ÑÐ°ÐºÐ°Ð¼. Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ â Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÐ¸ ÑÐµÑÐ¸ Ð¿ÑÑÐµÐ¼ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸ LSTM-ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ðº ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐ¼ Ð°ÑÐ°ÐºÐ°Ð¼. Ð Ð´Ð¸Ð¿Ð»Ð¾Ð¼Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð°ÑÐ°ÐºÐ¸ Ð½Ð° Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¸Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ, Ð¾Ð¿Ð¸ÑÐ°Ð½Ð° ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÐ¼Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ ÑÐ³ÑÐ¾Ð·, Ð° ÑÐ°ÐºÐ¶Ðµ Ð²ÑÐ±ÑÐ°Ð½Ñ Ð¸ Ð¾Ð±ÑÑÐ»Ð¾Ð²Ð»ÐµÐ½Ñ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÐ¼ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½Ð°ÑÑÑÐ¸ÑÐµÐ»Ñ. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð°ÑÐ°ÐºÐ° Â«Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸ÑÂ» ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ°. ÐÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ Ð¸ Ð²ÑÐ±ÑÐ°Ð½Ñ Ð¼ÐµÑÐ¾Ð´Ñ Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð°ÑÐ°Ðº. Ð­ÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¾Ð´ÐµÑÐ¶Ð°Ñ Ð¾ÑÐµÐ½ÐºÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð²ÑÐ±ÑÐ°Ð½Ð½ÑÑ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð°ÑÐ°Ðº Ð¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð·Ð°ÑÐ¸ÑÑ, Ð¿Ð¾ Ð¾ÑÐ½Ð¾ÑÐµÐ½Ð¸Ñ Ðº Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½Ð¾Ð¼Ñ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿Ñ LSTM-ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ°. ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð¸ÑÑ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð²ÑÐ±ÑÐ°Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½Ð°ÑÑÑÐ¸ÑÐµÐ»ÐµÐ¹. ÐÑÐ»Ð° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð°ÑÐ°ÐºÐ° Â«Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸ÑÂ» ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ° Ð¸ Ð¾Ð¿ÑÐ¾Ð±Ð¾Ð²Ð°Ð½ Ð¼ÐµÑÐ¾Ð´ Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ¾Ð´Ð° Ð°ÑÐ°Ðº Ð¿ÑÑÐµÐ¼ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÑ Ð¿ÑÐ¸Ð¼ÐµÑÐ°Ñ. LSTM-ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾Ñ ÑÐµÑÐµÐ²Ð¾Ð³Ð¾ ÑÑÐ°ÑÐ¸ÐºÐ°, Ð¿ÑÐ¸ Ð¾ÑÑÑÑÑÑÐ²Ð¸Ð¸ Ð·Ð°ÑÐ¸ÑÑ, Ð¾ÐºÐ°Ð·Ð°Ð»ÑÑ ÑÑÐ·Ð²Ð¸Ð¼ÑÐ¼ Ð¿ÐµÑÐµÐ´ ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐ¼Ð¸ Ð°ÑÐ°ÐºÐ°Ð¼Ð¸. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿, Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð½Ð¾Ð¹ ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÑÑ Ðº ÑÐ°ÐºÐ¸Ð¼ Ð°ÑÐ°ÐºÐ°Ð¼, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½ Ð´Ð»Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð°ÑÐ°Ðº Ð² ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÑÐ¸.
description: The given work is devoted to investigation of LSTM classifierâs robustness to adversarial attacks. The goal of the work is ensuring network security through enhancement of LSTM classifierâs robustness to adversarial attacks. In this diploma, adversarial attacks on machine learning models with their classification were studied. Threat model was determined and described, attacker models were selected and defined. Poisoning attack on classifier was proposed. Investigation and selection of defence methods against adversarial attacks were done. Experimental research includes analysis of effectiveness of selected adversarial attacks and defence methods towards implemented LSTM prototype and with respect to attacker models. Also poisoning attack method was implemented along with proposed defence method which is based on training with adversarial samples. LSTM classifier of network traffic without defence appeared to be vulnerable to adversarial attacks. As a result of the work, a prototype with improved robustness to adversarial attacks was developed which can be used for detection of attacks in modern networks.