Abstract:
Malware continues to pose a significant threat to computer systems and networks, making its detection and classification crucial for effective cybersecurity. This abstract presents an overview of a research study focused on classifying malware into families based on file contents and characteristics. The proposed approach aims to enhance malware analysis and response strategies by enabling security practitioners to identify and categorize malware samples more accurately.
The study employs a combination of static and dynamic analysis techniques to extract relevant features from malware files. Static analysis involves examining the file’s content without executing it, while dynamic analysis involves observing the behavior of the malware in a controlled environment. The extracted features encompass both structural attributes, such as file format and headers, and behavioral attributes, including API calls, network communication patterns, and system modifications.
To establish a classification framework, various machine learning algorithms are explored, including supervised learning techniques such as support vector machines (SVM), random forests, and deep learning models. Training datasets comprising labeled malware samples from known families are utilized to train the models and develop robust classification models. Additionally, feature selection algorithms are employed to identify the most discriminative features for accurate classification.
The research also focuses on handling challenges such as obfuscation techniques employed by malware authors to evade detection. Advanced feature extraction and analysis methods, such as opcode-based analysis and code similarity analysis, are explored to overcome these challenges and improve the accuracy of malware classification.
The proposed approach is evaluated using a large dataset of real-world malware samples collected from diverse sources. The performance of the classification models is assessed in terms of accuracy, precision, recall, and F1-score. Comparative analysis is conducted to identify the strengths and weaknesses of different machine learning algorithms and feature selection techniques.
The results demonstrate that the proposed approach effectively classifies malware into families based on file contents and characteristics. The developed models show promising accuracy rates, providing security practitioners with valuable insights for identifying and mitigating malware threats efficiently. The research contributes to the development of more robust and proactive cybersecurity measures, enabling organizations to enhance their defenses against evolving malware threats.
Keywords: Malware classification, file analysis, feature extraction, machine learning, static analysis, dynamic analysis, cybersecurity.
MALWARE CLASSIFICATION INTO FAMILIES BASED ON FILE CONTENTS AND CHARACTERISTICS, GET MOREÂ Â COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
