Vui lòng dùng định danh này để trích dẫn hoặc liên kết đến tài liệu này: https://elib.vku.udn.vn/handle/123456789/1008
Nhan đề: An Evaluation of Image-Based Malware Classification Using Machine Learning
Tác giả: Tran, The Son
Lee, Chando
Le, Minh Hoa
Nauman, Aslam
Moshin, Raza
Nguyen, Quoc Long
Từ khoá: Deep Learning
CNN
k- NN
Naïve Bayes
SVM
Image-Based malware classification
Năm xuất bản: 2020
Nhà xuất bản: Springer Publishing
Trích dẫn: https://link.springer.com/chapter/10.1007/978-3-030-63119-2_11
Tóm tắt: This paper investigates the image-based malware classification using machine learning techniques. It is a recent approach for malware classification in which malware binaries are converted into images (i.e. malware images) prior to feeding machine learning models, i.e. k-nearest neighbour (k-NN), Naïve Bayes (NB), Support Vector Machine (SVM) or Convolution Neural Networks (CNN). This approach relies on image texture to classify a malware instead of signatures or behaviours of malware collected via malware analysis, thus it does not encounter a problem if the signatures of a new malware variant has not been collected or the behaviours of a new malware variant has not been updated. This paper evaluates classification performance of various machine learning classifiers (i.e. k-NN, NB, SVM, CNN) fed by malware images in various dimensions (i.e., 128 × 128, 64 × 64, 32× 32, 16 × 16). The experiment results achieved on three different datasets including Malimg, Malheur and BIG2015 show that kNN outperforms others on three datasets with high accuracy (i.e. 97.9%, 94.41% and 95.63% respectively). On the contrary, NB showed its weakness on imagebased malware classification. Experiment results also indicate that the accuracy of the k-NN reaches the highest value at the input image size of 32 × 32 and tends to reduce if too many feature information provided by large input images, i.e. 64 × 64, 128 × 128.
Mô tả: Scientific Paper; Pages: 125-138
Định danh: http://elib.vku.udn.vn/handle/123456789/1008
ISBN: 978-3-030-63119-2 (eBook)
978-3-030-63118-5
ISSN: 1865-0937 (electronic)
1865-0929
Bộ sưu tập: 12th International Conference on Computational Collective Intelligence - ICCCI 2020

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