@article{ref1,
title="Intrusion detection method for internet of vehicles based on parallel analysis of spatio-temporal features",
journal="Sensors (Basel)",
year="2023",
author="Xing, Ling and Wang, Kun and Wu, Honghai and Ma, Huahong and Zhang, Xiaohui",
volume="23",
number="9",
pages="-",
abstract="The problems with network security that the Internet of Vehicles (IoV) faces are becoming more noticeable as it continues to evolve. Deep learning-based intrusion detection techniques can assist the IoV in preventing network threats. However, previous methods usually employ a single deep learning model to extract temporal or spatial features, or extract spatial features first and then temporal features in a serial manner. These methods usually have the problem of insufficient extraction of spatio-temporal features of the IoV, which affects the performance of intrusion detection and leads to a high false-positive rate. To solve the above problems, this paper proposes an intrusion detection method for IoV based on parallel analysis of spatio-temporal features (PA-STF). First, we built an optimal subset of features based on feature correlations of IoV traffic. Then, we used the temporal convolutional network (TCN) and long short-term memory (LSTM) to extract spatio-temporal features in the IoV traffic in a parallel manner. Finally, we fused the spatio-temporal features extracted in parallel based on the self-attention mechanism and used a multilayer perceptron to detect attacks in the Internet of Vehicles. The experimental results show that the PA-STF method reduces the false-positive rate by 1.95% and 1.57% on the NSL-KDD and UNSW-NB15 datasets, respectively, with the accuracy and F1 score also being superior.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23094399",
url="http://dx.doi.org/10.3390/s23094399"
}