@inproceedings{4bab58b2b9fe461eaa4195f95c9545fd,
title = "A CNN and Image-Based Approach for Malware Analysis",
abstract = "Malware attacks have various types, patterns, and volumes and have become more sophisticated and severe. Using machine learning to classify and detect malware is one of the approaches to mitigate malware attacks. However, malware classification suffers from some challenges such as the time required in manipulating a huge number of malware files. This paper proposes a Convolutional Neural Network (CNN) model and a pre-processing approach to solve the aforementioned issue. The contribution of this paper is based on converting the dataset into RGB images followed by scale maximization step. In addition, the paper proposes a preprocessing approach for the input datum of images. The results prove that the proposed preprocessing methods have a strong impact on enhancing the overall accuracy by increasing the accuracy from 92.5\% to 98\%.",
keywords = "Convolutional Neural Network (CNN), Machine Learning, Malware Analysis",
author = "Aya Migdady and Lara Smadi and Qussai Yaseen",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 ; Conference date: 23-11-2022 Through 25-11-2022",
year = "2022",
doi = "10.1109/ETCEA57049.2022.10009748",
language = "English",
series = "2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022 - Proceedings",
address = "United States",
}