Machine learning in cybersecurity: exploring approaches for malware detection and categorization
Başlık çevirisi mevcut değil.
- Tez No: 938459
- Danışmanlar: YRD. DOÇ. DR. AHMET ŞENOL
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2024
- Dil: İngilizce
- Üniversite: Üsküdar Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Siber Güvenlik Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 54
Özet
This study focuses on the complex task of malware detection by creating, assessing, and contrasting machine learning and deep learning models for the identification and categorization of malware contained in both text files as well as executable (.EXE) file types. The research employs a rigorous quantitative research methodology and utilises two main datasets. One of them being Dike Dataset for Portable executable and object linking and embedding files, and the Malware Categorization Dataset for .txt file formats. This study will utilize Neural Networks (ANN), Artificial Neural Networks (ANN), Random Forest Classifier, AdaBoost Classifier, and Convolutional Neural Networks (CNN1D), are thoroughly evaluated and analysed using metrics such as accuracy, precision, recall, F1 score, and confusion matrices. The study demonstrates the improved effectiveness of the HistGradientBoosting Classifier and Random Forest Classifier in analysing text and EXE files, respectively. It also compares their performance with that of ANN and CNN1D models. The integration of these models into a user-centric Streamlit application expands the availability of sophisticated virus detection approaches. The results highlight the strong capacity of integrating machine learning and deep learning to enhance cybersecurity technologies and processes. This incorporation into a functional application represents a significant improvement in cybersecurity measures designed for both personal and institutional utilisation.
Özet (Çeviri)
This study focuses on the complex task of malware detection by creating, assessing, and contrasting machine learning and deep learning models for the identification and categorization of malware contained in both text files as well as executable (.EXE) file types. The research employs a rigorous quantitative research methodology and utilises two main datasets. One of them being Dike Dataset for Portable executable and object linking and embedding files, and the Malware Categorization Dataset for .txt file formats. This study will utilize Neural Networks (ANN), Artificial Neural Networks (ANN), Random Forest Classifier, AdaBoost Classifier, and Convolutional Neural Networks (CNN1D), are thoroughly evaluated and analysed using metrics such as accuracy, precision, recall, F1 score, and confusion matrices. The study demonstrates the improved effectiveness of the HistGradientBoosting Classifier and Random Forest Classifier in analysing text and EXE files, respectively. It also compares their performance with that of ANN and CNN1D models. The integration of these models into a user-centric Streamlit application expands the availability of sophisticated virus detection approaches. The results highlight the strong capacity of integrating machine learning and deep learning to enhance cybersecurity technologies and processes. This incorporation into a functional application represents a significant improvement in cybersecurity measures designed for both personal and institutional utilisation.
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