Research Article
Intrusion Detection for WiFi Network: A Deep Learning Approach
@INPROCEEDINGS{10.1007/978-3-030-06158-6_10, author={Shaoqian Wang and Bo Li and Mao Yang and Zhongjiang Yan}, title={Intrusion Detection for WiFi Network: A Deep Learning Approach}, proceedings={Wireless Internet. 11th EAI International Conference, WiCON 2018, Taipei, Taiwan, October 15-16, 2018, Proceedings}, proceedings_a={WICON}, year={2019}, month={1}, keywords={Wi-fi network Network intrusion detection Deep learning}, doi={10.1007/978-3-030-06158-6_10} }- Shaoqian Wang
Bo Li
Mao Yang
Zhongjiang Yan
Year: 2019
Intrusion Detection for WiFi Network: A Deep Learning Approach
WICON
Springer
DOI: 10.1007/978-3-030-06158-6_10
Abstract
With the popularity and development of Wi-Fi network, network security has become a key concern in the recent years. The amount of network attacks and intrusion activities are growing rapidly. Therefore, the continuous improvement of Intrusion Detection Systems (IDS) is necessary. In this paper, we analyse different types of network attacks in wireless networks and utilize Stacked Autoencoder (SAE) and Deep Neural Network (DNN) to perform network attack classification. We evaluate our method on the Aegean WiFi Intrusion Dataset (AWID) and preprocess the dataset by feature selection. In our experiments, we classified the network records into 4 types: normal record, injection attack, impersonation attack and flooding attack. The classification accuracies we achieved of these 4 types of records are 98.4619, 99.9940, 98.3936 and 73.1200, respectively.