Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
- Title
- Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
- Creator
- Jeyalakshmi V.S.; Krishnan N.; Jayapriya J.
- Description
- The complexity of classifying malware is high since it may take many forms and is constantly changing. With the help of transfer learning and easy access to massive data, neural networks may be able to easily manage this problem. This exploratory work aspires to swiftly and precisely classify malware shown as grayscale images into their various families. The VGG-16 model, which had already been trained, was used together with a learning algorithm, and the resulting accuracy was 88.40%. Additionally, the Inception-V3 algorithm for classifying malicious images into family members did significantly improve their unique approach when compared with the ResNet-50. The proposed model developed using a convolution neural network outperformed the others and correctly identified malware classification 94.7% of the time. Obtaining an F1-score of 0.93, our model outperformed the industry-standard VGG-16, ResNet-50, and Inception-V3. When VGG-16 was tuned incorrectly, however, it lost many of its parameters and performed poorly. Overall, the malware classification problem is eased by the approach of converting it to images and then classifying the generated images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Lecture Notes in Networks and Systems, Vol-537 LNNS, pp. 39-51.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Deep learning; Inception-V3; Malware; ResNet-50; Transfer learning; VGG-16
- Coverage
- Jeyalakshmi V.S., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India; Krishnan N., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India; Jayapriya J., Department of Computer Science (YPR Campus), Christ University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981993009-8
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jeyalakshmi V.S.; Krishnan N.; Jayapriya J., “Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19785.