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Online anomaly detection with bandwidth optimized hierarchical kernel density estimators
We propose a novel unsupervised anomaly detection algorithm that can work for sequential data from any complex distribution in a truly online framework with mathematically proven strong performance guarantees. First, a ...
A novel distributed anomaly detection algorithm based on support vector machines
In this paper, we study anomaly detection in a distributed network of nodes and introduce a novel algorithm based on Support Vector Machines (SVMs). We first reformulate the conventional SVM optimization problem for a ...
Unsupervised anomaly detection with LSTM neural networks
We investigate anomaly detection in an unsupervised framework and introduce long short-term memory (LSTM) neural network-based algorithms. In particular, given variable length data sequences, we first pass these sequences ...
Client-specific anomaly detection for face presentation attack detection
One-class anomaly detection approaches are particularly appealing for use in face presentation attack detection (PAD), especially in an unseen attack scenario, where the system is exposed to novel types of attacks. This ...