title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ¸ÑÑÐµÐ¼Ñ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ Ð°ÑÐ°Ðº ÑÐºÐ»Ð¾Ð½ÐµÐ½Ð¸Ñ Ð¸ "Ð¾ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ" Ð²Ñ Ð¾Ð´Ð½ÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
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: Development of the system for the detection of evasion and data "poisoning" attacks on machine learning systems
Creators: name: Ð§Ð°Ð³Ð¾ÑÐºÐ¸Ð½, ÐÐ»ÐµÐºÑÐ°Ð½Ð´Ñ
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: 2022
Subjects: ÑÐ¾ÑÑÑÐ·Ð°ÑÐµÐ»ÑÐ½ÑÐµ Ð°ÑÐ°ÐºÐ¸
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
ÑÑÐ²ÑÑÐ²Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ Ð´Ð°Ð½Ð½ÑÑ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð°Ð½Ð¾Ð¼Ð°Ð»Ð¸Ð¹
ÑÑÐ·Ð²Ð¸Ð¼Ð¾ÑÑÐ¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð²
adversarial attack
machine learning
data sensitivity
anomaly detection
algorithm vulnerability

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description: The topic of the graduate qualification work is Â«Development of the system for the detection of evasion and data "poisoning" attacks on machine learning systemsÂ».The goal of this study â development of a method for detecting anomalies associated with poisoning and evasion attacks, in input data for the MO data systems..The subject of the work is: algorithms that lay in different classes of ML; data types used as input data in ML; adversarial attacks.The research set the following goals: analysis and systematization of weaknesses and sensitivities of ML systems; classification of actual threats specific for AI and ML systems; research and listing of the vulnerabilities of standard ML algorithms to poisoning and evasion attacks; development of the software for data anomalies detection in input data.During the work were done research of the possible attackâs strategies on a ML system; classification of those attacks and attacker; research of the sensitivity of algorithms underlying in various classes of ML.As a result of the work, a classification of attacks on AI systems was formulated. The sensitivity features of the algorithms underlying different classes of ML problems to different input data were formulated. The software for anomalies search in the input data of ML systems was developed.The results could be used as: guidelines for classification of ML threats; the basis for the course about the vulnerabilities of the ML for students of the information security university programs; recommendations for the design of ML systems; a software layer for ensuring the security of ML systems.