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ToolsSon, Tran The, Lee, Chando, Le-Minh, Hoa, Aslam, Nauman, Raza, Moshin and Long, Nguyen Quoc (2020) An Evaluation of Image-Based Malware Classification Using Machine Learning. In: Advances in Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings. Communications in Computer and Information Science . Springer, VNM, pp. 125-138. ISBN 9783030631185
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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 k-NN 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 image-based 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.
| Item Type: | Book Section |
|---|---|
| Additional Information: | Publisher Copyright: © 2020, Springer Nature Switzerland AG. |
| Uncontrolled Keywords: | cnn,deep learning,image-based malware classification,k- nn,naïve bayes,svm,computer science(all),mathematics(all) ,/dk/atira/pure/subjectarea/asjc/1700 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Cyber Intelligence and Networks |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 16 Jun 2025 10:31 |
| Last Modified: | 17 Jun 2025 06:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/99546 |
| DOI: | 10.1007/978-3-030-63119-2_11 |
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