Mechanized Detection and Extraction of Malware Using Deep Learning Approaches
Keywords:
Cyber security, Gray Level Run Length matrix, Artificial Intelligence, Deep Learning, Multi-Layer convolution neural networks.Abstract
Malware creation is developing a considerable dangerous to the individuals as well as an organization. Protecting against these risks is continually being processed by the digital protection cyber specialists. The obscurity of categorizing malware is high since it might take many patterns and is continually evolving. With the support of artificial intelligence can undoubtedly access the large information, neural networks can be able to deal this problem very easily. This research aims to furnish effective by applying convolutional neural network with multi-layers to handle the situations of using imbalanced datasets. The proposed model developed by applying a Convolutional Neural Network with multi layers performed best to categorize the malware with 96.25% of accuracy. Generally, the malware classification problem is eased by the approach of converting it to binary images and then classifying the generated images.
Downloads
References
Muhammad Shoaib Akhtar (2022) Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time.
Rong Wang (2021) Malware Detection using CNN via word embedding in cloud computing infrastructure.
Kaspersky Lab (2021). The number of new malicious files detected every day increases by 5.2% to 360,000 in 2020. [online] www.kaspersky.com. https://www.kaspersky.com/about/press-releases/2020_the-number-of-new- malicious-files-detected-every-day-increases-by-52-to-360000-in-2020> .
ISC2 (2021). 2021 Cybersecurity Workforce Study. [online] www.isc2.org, ISC2, pp.24–25. https://www.isc2.org//-/media/ISC2/Research/2021/ISC2-Cybersecurity-Workforce-Study-2021.ashx>.
SonicWall (2021, 2023). SonicWall Cyber Threat Report. [online] https://www.sonicwall.com/, Milpitas, CA: SonicWall Inc.,p.58. https://www.sonicwall.com/resources/white-papers/2021-sonicwall-cyber-threat-report/ >.
Krithika V. (2021). Malware and Benign Detection Using Convolutional Neural Network .
Anwar A. (2021). Difference between Local Response Normalization and Batch Normalization. [online] Medium. http://towardsdatascience.com/difference-between-local-response-normalization-and-batch-normalization- 272308c034ac> .
Mallet H. (2020). Malware Classification using Convolutional Neural Networks — Step by Step Tutorial. [online] Medium. https://towardsdatascience.com/malware-classification-using-convolutional-neural-networks-step-by- step-tutorial-a3e8d97122f>.
Véstias M.P. (2019). A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing. Algorithms, 12(8), p.154.
Bhodia N., Prajapati P., Troia F. and Stamp M. (2019). Transfer Learning for Image-Based Malware Classification.
Le Q., Boydell O., Mac Namee B. and Scanlon M. (2018). Deep learning at the shallow end: Malware classification for non-domain experts. Digital Investigation, [online] 26, pp.S118–S126. https://www.sciencedirect.com/science/article/pii/S1742287618302032>.
Gibert D., Matteu C., Planes J. and Vicens R. (2018). Using convolutional neural networks for classification of malware represented as images. Journal of Computer Virology and Hacking Techniques, 15(1), pp.15–28.
Nataraj L., Karthikeyan S., Jacob, G. and Manjunath B.S. (2011). Malware images. Proceedings of the 8th International Symposium on Visualization for Cyber Security - VizSec ’11.
Raff E., Barker J., Sylvester J., Brandon R., Catanzaro B. and Nicholas C. (2017). Malware Detection by Eating a Whole EXE.
Microsoft (n.d.). An In-Depth Look into the Win32 Portable Executable File Format, Part 2: Figures. [online] bytepointer.com. https://bytepointer.com/resources/pietrek_in_depth_look_into_pe_format_pt2_figures.htm>.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
