title: ÐÐ°ÑÐ¸ÑÐ° Ð¼Ð¾Ð±Ð¸Ð»ÑÐ½ÑÑ ÑÑÑÑÐ¾Ð¹ÑÑÐ² Ð¿Ð¾Ð´ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸ÐµÐ¼ Android Ð¾Ñ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
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: Protection of mobile devices running Android from malicious software using an artificial neural network
Creators: name: ÐÐ¾ÑÐ¾Ð»ÑÐºÐ¾Ð², ÐÐ»ÐµÐºÑÐµÐ¹
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: 2020
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
ÑÐ¸ÑÑÐµÐ¼Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²Ð¿Ð¾
Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ðµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ðµ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ
ÑÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ðµ ÑÑÐºÐ¾ÑÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹
malware detection system
malicious software
static analysis
deep learning
hardware acceleration of neural computing

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description: The subject of the graduate qualification work is "Protection of mobile devices running Android from malicious software using an artificial neural network". This work is devoted to improving the security of devices running Android by developing tools for detecting malicious software using an artificial neural network. Tasks that were solved in the course of the study: 1. To analyse the methods of detection of malware for Android OS. 2. Explore the features of the Android OS software architecture. 3. Develop an approach to detecting malicious software using an artificial neural network. 4. Develop a program layout that implements the proposed approach. 5. Determine the optimal performance parameters and evaluate the effectiveness of the developed layout. This paper presents a research of existing problems of information security of applications on the Android operating system, various approaches to malware detection. A research was made on the possibilities of hardware acceleration of computing neural networks on mobile devices on the Android platform with the help of graphics and signal processors. Different implementations of computing acceleration are considered and tested on various devices. A method for detecting Android malware using artificial neural networks is offered, and the accuracy and effectiveness of malware detection by the developed software prototype is assessed.