Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security
| Author | Qiao, Yanchen |
| Author | Zhang, Weizhe |
| Author | Du, Xiaojiang |
| Author | Guizani, Mohsen |
| Available date | 2022-10-11T18:33:01Z |
| Publication Date | 2022-02-01 |
| Publication Name | ACM Transactions on Internet Technology |
| Identifier | http://dx.doi.org/10.1145/3436751 |
| Citation | Qiao, Y., Zhang, W., Du, X., & Guizani, M. (2021). Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security. ACM Transactions on Internet Technology (TOIT), 22(1), 1-22. |
| ISSN | 15335399 |
| Abstract | With the construction of smart cities, the number of Internet of Things (IoT) devices is growing rapidly, leading to an explosive growth of malware designed for IoT devices. These malware pose a serious threat to the security of IoT devices. The traditional malware classification methods mainly rely on feature engineering. To improve accuracy, a large number of different types of features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve these issues, a malware classification method based on Word2Vec and Multilayer Perception (MLP) is proposed in this article. First, for one malware sample, Word2Vec is used to calculate a word vector for all bytes of the binary file and all instructions in the assembly file. Second, we combine these vectors into a 256x256x2-dimensional matrix. Finally, we designed a deep learning network structure based on MLP to train the model. Then the model is used to classify the testing samples. The experimental results prove that the method has a high accuracy of 99.54%. |
| Sponsor | This work was supported in part by the Key-Area Research and Development Program of Guangdong Province (2019B010136001), the Basic and Applied Basic Research Major Program for Guangdong Province (2019B030302002), and the Science and Technology Planning Project of Guangdong Province (LZC0023 and LZC0024). Authors’ addresses: Y. Qiao, Cyberspace Security Research Center, Peng Cheng Laboratory, No. 2 Xingke 1st Street, Shen-zhen, China, 518000; email: qiaoych@pcl.ac.cn; W. Zhang, School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin, China, 150001, Cyberspace Security Research Center, Peng Cheng Laboratory, No. 2 Xingke 1st Street, Nanshan District, Shenzhen, China, 518000; email: wzzhang@hit.edu.cn; X. Du, Department of Computer and Information Sciences, Temple University, 1801 N. Broad Street, Philadelphia, USA, PA 19122; email: dxj@ieee.org; M. Guizani, Department of Compute Science and Engineering, Qatar University, University Street, Doha, Qatar; email: mguizani@ieee.org. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. 1533-5399/2021/09-ART10 $15.00 https://doi.org/10.1145/3436751 |
| Language | en |
| Publisher | Association for Computing Machinery |
| Subject | IoT Malware classification multilayer perception Word2Vec |
| Type | Article |
| Pagination | 1-22 |
| Issue Number | 1 |
| Volume Number | 22 |
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