Unsupervised Acoustic Condition Monitoring with Riemannian Geometry

Lifshits, Pavel; Talmon, Ronen

doi:10.1109/mlsp49062.2020.9231898

Cited by 1 publication

(2 citation statements)

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“…Covariance matrices have proven to be powerful features to extract spatio-temporal characteristics in a variety of applications, such as time series classification [10], network anomaly detection [11], and brain-computer interface [12]. The sample covariance matrices (SCMs) consider mutual relationships between multivariate distributions [13]. In this work, we not only exploit the mutual relationships between variables through SCMs, but also the geometry of covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we not only exploit the mutual relationships between variables through SCMs, but also the geometry of covariance matrices. In general, covariance matrices form a Riemannian hyper-cone embedded with distance metric that facilitates discriminate information extraction in the manifold [12,13]. The main contributions of this paper can be summarised as follows.…”

Section: Introductionmentioning

confidence: 99%

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Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold

Singh¹,

Jang-Jaccard²

2021

Preprint

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Network traffic data is combination of different data bytes packet under different network protocols. These traffic packets have complex time-varying non-linear relationships. Existing state of the art method rise up to this challenge by fusing features into multiple subset based on correlations and using hybrid classification techniques that extract spatial and temporal characteristics. This offen 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 extracts spatio-temporal characteristics of network traffic data for detecting malicious network traffic behaviour. The covariance matrices in our proposed method not just naturally encodes the mutual relationships between different network traffic values but also have well defined geometry that falls in Riemannian manifold. Riemannian manifold is embedded with distance metrices that facilitates extracting descriminative features for detecting malicious network traffic. We evaluated our model on an NSL-KDD and UNSW-NB15 datasets and shown our proposed method significantly outperforms the conventional method and other existing studies on the dataset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%