  Network traffic data is a combination of different data bytes packets under
different network protocols. These traffic packets have complex time-varying
non-linear relationships. Existing state-of-the-art methods rise up to this
challenge by fusing features into multiple subsets based on correlations and
using hybrid classification techniques that extract spatial and temporal
characteristics. This often requires high computational cost and manual support
that limit them for real-time processing of network traffic. To address this,
we propose a new novel feature extraction method based on covariance matrices
that extract spatial-temporal characteristics of network traffic data for
detecting malicious network traffic behavior. The covariance matrices in our
proposed method not just naturally encode the mutual relationships between
different network traffic values but also have well-defined geometry that falls
in the Riemannian manifold. Riemannian manifold is embedded with distance
metrics that facilitate extracting discriminative features for detecting
malicious network traffic. We evaluated our model on NSL-KDD and UNSW-NB15
datasets and showed our proposed method significantly outperforms the
conventional method and other existing studies on the dataset.
