| Title | Network Intrusion Detection Based on BiSRU and CNN |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ding, Shanshuo, Wang, Yingxin, Kou, Liang |
| Conference Name | 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS) |
| Keywords | BiSRU, CNN, composability, feature extraction, Intrusion detection, machine learning, machine learning algorithms, Metrics, network intrusion detection, Neural networks, pubcrawl, resilience, Resiliency, spatial features, telecommunication traffic, temporal features, Training |
| Abstract | In recent years, with the continuous development of artificial intelligence algorithms, their applications in network intrusion detection have become more and more widespread. However, as the network speed continues to increase, network traffic increases dramatically, and the drawbacks of traditional machine learning methods such as high false alarm rate and long training time are gradually revealed. CNN(Convolutional Neural Networks) can only extract spatial features of data, which is obviously insufficient for network intrusion detection. In this paper, we propose an intrusion detection model that combines CNN and BiSRU (Bi-directional Simple Recurrent Unit) to achieve the goal of intrusion detection by processing network traffic logs. First, we extract the spatial features of the original data using CNN, after that we use them as input, further extract the temporal features using BiSRU, and finally output the classification results by softmax to achieve the purpose of intrusion detection. |
| DOI | 10.1109/MASS52906.2021.00026 |
| Citation Key | ding_network_2021 |
Network Intrusion Detection Based on BiSRU and CNN