title: ÐÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð¿Ð¾ÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÑ ÑÑÐ°ÑÑÐºÐ¾Ð² ÐºÐ¾Ð´Ð° Ð² Android-Ð¿ÑÐ¸Ð»Ð¾Ð¶ÐµÐ½Ð¸ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð³Ð¾ 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: Identification of potentially malicious parts of code in Android application using deep learning
Creators: name: ÐÐ²Ð°Ð½ÐµÐ½ÐºÐ¾, ÐÐ²Ð°Ð½
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: 2024
Subjects: Ð²Ð¸ÑÑÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÐ¹ ÐºÐ¾Ð´
virus analysis
deep learning
malicious code

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description: The topic of the graduation qualification work: Â«Identification of Potentially Malicious Code Segments in Android Applications Using Deep LearningÂ». The goal of this work is to identify potentially malicious code segments in Android applications based on the analysis of the control flow graph using deep learning methods. The subject of the study is the application of deep learning to detect potentially dangerous segments in the code of Android applications. The tasks solved in the course of the research are: Â  Â  1. Investigate the architectural features of the Android operating system and the structure of APK files. Â  Â  2. Determine the characteristics of Java code in Android applications related to information security threats. Â  Â  3. Study deep learning models and make a reasoned choice of the most suitable model to achieve the set goal. Â  Â  4. Develop a method for identifying malicious code segments based on the analysis of the control flow graph using deep learning. Â  Â  5. Develop a software prototype that provides the identification of potentially malicious code segments, and evaluate its effectiveness. During the work, the architecture of the Android operating system, the structure of APK files, and methods for finding and detecting malicious code segments were investigated. As a result of the work, a software prototype based on deep learning was developed, capable of effectively identifying potentially malicious code segments in Android applications.