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| Title | A Network Intrusion Detection Method Based On Cnn And Cbam |
|---|---|
| ID_Doc | 2938 |
| Authors | Liu Y.; Kang J.; Li Y.; Ji B. |
| Year | 2021 |
| Published | IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2021 |
| DOI | http://dx.doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484553 |
| Abstract | The arrival of the 5G era has opened a new era of the interconnection of everything for the world. Artificial intelligence, autonomous driving, and smart cities have all reached their peaks due to the advent of 5G. However, the network environment is becoming more complex, and the types of cyberattacks are gradually increasing. Once the network device is attacked, the loss it brings cannot be calculated. The intrusion detection system is a very effective measure in protecting network security. In this paper, we proposed a novel network intrusion detection model based on Convolutional Neural Network, which introduces the Convolutional Block Attention Module. Experiments are constructed based on the CIC-IDS2018 dataset. We compare the proposed model with DNN and CNN. The results show that the accuracy of the proposed model can reach 99.8752% in the two-classification case and 97.2887% in the multi-classification case. © 2021 IEEE. |
| Author Keywords | CIC-IDS2018 dataset; Convolutional Block Attention Module; Convolutional Neural Network; Network Intrusion Detection |
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