Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security

Yanchen Qiao, Weizhe Zhang, Xiaojiang Du, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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%.

Original languageEnglish
Article number3436751
JournalACM Transactions on Internet Technology
Volume22
Issue number1
DOIs
StatePublished - Feb 2022

Keywords

  • IoT
  • Malware classification
  • Word2Vec
  • multilayer perception

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