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There are a number of studies that use different graph based features to detect anomalies.
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The technique first utilizes a Bayesian classifier based on low-level image features to detect the lumen, epithelial cell cytoplasm, and epithelial nuclei.
The proposed approach in this study is first of its kind to convert the NetFlow features available from CTU-13 dataset into graph based features and use these graph features to detect botnets.
They analyzed the response of the bicoherence magnitude and phase features to detect image splicing based on the proposed model.
Hence, content based features are used to detect the attacks.
Hence, it is a useful feature to detect a human.
Seven graph based features are used to characterize the topological structure of the network and utilize them to detect botnets.
NetFlow based features (or flow based features) have been used to detect anomalies including botnets in a high speed, large volume data networks.
Clustering is a popular approach taken by researchers to detect botnets using flow based features.
Fairly recently, a decision tree classifier has been used by Zhao et al. [19] to detect botnets by investigating 12 flow based features.
However, the hybrid feature space of SAAC and PSSM performs worse compared to the PSSM based features.
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