title: Machine Learning-Based Approaches for Malware Detection in PE Files
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: ÐÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ Ð² ÑÐ°Ð¹Ð»Ð°Ñ ÑÐ¾ÑÐ¼Ð°ÑÐ° PE
Creators: name: ÐÐ°ÑÑÐ², ÐÐ¸ÑÐ°Ð¸Ð»
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: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
malware
detection
Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ
Ð²ÑÐµÐ´Ð¾Ð½Ð¾ÑÐ½ÑÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ñ

description: Malware poses a significant threat to the security and stability of computing systems. Traditional signature-based and behavior-based detection methods often struggle to keep up with the rapidly evolving landscape of malware. This research work âMachine Learning-Based Approaches for Malware Detection in PE Filesâ explores the application of machine learning techniques to enhance malware detection. The thesis first provides an overview of common malware detection methods, including signature-based detection, behavior-based detection, sandbox analysis, and heuristic analysis. It also examines contemporary antimalware software and the role of machine learning in malware detection. The theoretical framework and methodology sections introduce the PE file format, datasets used (DikeDataset and VirusShare.com samples), and various machine learning models evaluated, such as AdaBoost, LightGBM, Random Forest, CatBoost The experimental setup involves data preprocessing, exploratory data analysis, model initialization and parameter optimization, batch model training, and interpretation of the model predictions using the LIME library. The results discussion covers the evaluation metrics (accuracy, precision, recall, and F1 score) and a comparison of the performance of the implemented machine learning models. The findings of this thesis demonstrate the potential of machine learning-based approaches in enhancing malware detection in PE files. The results indicate that ensemble methods, such as LightGBM and XGBoost, outperform other models in terms of detection accuracy and overall performance. The thesis also explores potential improvements and future research directions in this domain.
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