STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion
Detection System for Intelligent Connected Vehicles
- URL: http://arxiv.org/abs/2204.10990v1
- Date: Sat, 23 Apr 2022 04:22:58 GMT
- Title: STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion
Detection System for Intelligent Connected Vehicles
- Authors: Mu Han, Pengzhou Cheng, and Fengwei Zhang
- Abstract summary: We present a novel model for automotive intrusion detection by spatial-temporal correlation features of in-vehicle communication traffic (STC-IDS)
Specifically, the proposed model exploits an encoding-detection architecture. In the encoder part, spatial and temporal relations are encoded simultaneously.
The encoded information is then passed to the detector for generating forceful spatial-temporal attention features and enabling anomaly classification.
- Score: 7.301018758489822
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Intrusion detection is an important defensive measure for the security of
automotive communications. Accurate frame detection models assist vehicles to
avoid malicious attacks. Uncertainty and diversity regarding attack methods
make this task challenging. However, the existing works have the limitation of
only considering local features or the weak feature mapping of multi-features.
To address these limitations, we present a novel model for automotive intrusion
detection by spatial-temporal correlation features of in-vehicle communication
traffic (STC-IDS). Specifically, the proposed model exploits an
encoding-detection architecture. In the encoder part, spatial and temporal
relations are encoded simultaneously. To strengthen the relationship between
features, the attention-based convolution network still captures spatial and
channel features to increase the receptive field, while attention-LSTM build
important relationships from previous time series or crucial bytes. The encoded
information is then passed to the detector for generating forceful
spatial-temporal attention features and enabling anomaly classification. In
particular, single-frame and multi-frame models are constructed to present
different advantages respectively. Under automatic hyper-parameter selection
based on Bayesian optimization, the model is trained to attain the best
performance. Extensive empirical studies based on a real-world vehicle attack
dataset demonstrate that STC-IDS has outperformed baseline methods and cables
fewer false-positive rates while maintaining efficiency.
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