title: ÐÐ±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² 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° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Malware detection and classification models using machine learning approaches
Creators: name: ÐÑÐ¸Ð¼Ð¾Ð², ÐÐ°ÐºÑÐ¸Ð¼
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: 2023
Subjects: Ð¼Ð½Ð¾Ð³Ð¾ÐºÐ»Ð°ÑÑÐ¾Ð²Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼
ÐºÐ¸Ð±ÐµÑÐ±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ
multiclass classification
machine learning
malware analysis
malware detection
cybersecurity

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ðº Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ:ÐÐ¿Ð¸ÑÐ°Ð½Ð¸Ðµ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼Ð¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ðº ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð³Ð¾ ÐÐ.Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½Ð¾Ð¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿ÑÐ¾ÑÑÐ¾Ð³Ð¾ Ð½ÐµÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð³Ð¾ ÐÐ.Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½Ð¾Ð³Ð¾ ÐÐ.ÐÑÐµÐ½ÐºÐ° ÐºÐ°ÑÐµÑÑÐ²Ð° Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½ÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹.Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° ÑÑÐµÐ¼Ð° Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼, ÐºÐ¾ÑÐ¾ÑÐ°Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð±Ð°Ð¹Ñ Ð¸ÑÐ¿Ð¾Ð»Ð½ÑÐµÐ¼ÑÑ ÑÐ°Ð¹Ð»Ð¾Ð² Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Ð² Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ. ÐÑÐ¾Ð³Ð¾Ð²ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ, ÑÑÐ¾ F-Ð¼ÐµÑÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð½Ð¾Ð¹ ÑÑÐµÐ¼Ñ ÑÐ¾ÑÑÐ°Ð²Ð»ÑÑÑ 97% Ð² ÑÐ»ÑÑÐ°Ðµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ°.
description: This thesis focuses on the investigation and implementation of a neural network malware classifier that can classify an unseen file as malicious or benign. The following list represents the formulated objectives of this thesis:Provide an explanation of the proposed methodology approach chosen for the malware classification.Use a kNN-classifier to categorize samples in malware families.Design and train a CNN architecture to perform a multiclass classification of malware.A final summary of the key differences between the two approaches is listed, with a comparison of each approach.This thesis proposes a scheme to detect and classify malware using only image representations of the malware binaries. This approach involves converting malware binaries to grayscale images and subsequently training a CNN for classification. As a result, the best CNN model achieved an F1-Score of 97% of family homology classification based on the proposed scheme.