TY - JOUR
T1 - Malware Classification Based on Multilayer Perception and Word2Vec for IoT Security
AU - Qiao, Yanchen
AU - Zhang, Weizhe
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2022/2
Y1 - 2022/2
N2 - 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%.
AB - 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%.
KW - IoT
KW - Malware classification
KW - Word2Vec
KW - multilayer perception
UR - http://www.scopus.com/inward/record.url?scp=85119209184&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119209184&partnerID=8YFLogxK
U2 - 10.1145/3436751
DO - 10.1145/3436751
M3 - Article
AN - SCOPUS:85119209184
SN - 1533-5399
VL - 22
JO - ACM Transactions on Internet Technology
JF - ACM Transactions on Internet Technology
IS - 1
M1 - 3436751
ER -