Sentence examples for anomaly detection approaches from inspiring English sources

Extensive experiments were carried out both on real as well as simulated data sets, and we show that the proposed techniques provide significant accuracy and efficiency in detecting cyber attacks, compared to three well-known anomaly detection approaches.

The supervised anomaly detection approaches require the labeled dataset so as to establish anomaly detection models.

In the existing literature, several anomaly detection approaches for ITS have been proposed.

One problem with standard anomaly detection approaches is that there is little concern for the context of the data content.

Given this setup, our goal is to develop suitable target and anomaly detection approaches, and provide theoretical guarantees on their performances.

Then in Section 3, the assumed scenario as well as details of the proposed anomaly detection approach is outlined.

Our anomaly detection approach and the associated theoretical analysis are based on a "distance preservation" property of A, which is stated formally in (18).

To the best of our knowledge, this is the first time that the anomaly detection approach is applied for PUE attacks on CWSNs.

One of the difficulties with validating any anomaly detection approach is that the definition of an anomaly changes depending on the owner of the dataset, and their view over what should be considered anomalous.

Bayesian classification models have proved to be efficient tools for intrusion detection [46, 47]. Figure 7 shows an example of an anomaly detection approach based on Bayesian alert correlation [48] for IDS alerts.

In short, the main contributions of this paper are fourfold: (i) to employ unsupervised OPF for non-technical losses detection, (ii) to model the problem of NTL as being an anomaly detection task, (iii) to employ unsupervised OPF to estimate the parameters of the Gaussian distributions, and (iv) to present an anomaly detection approach based on unsupervised optimum-path forest.