Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants

Fixed-Background EM Algorithm for Semi-Supervised Anomaly Detection

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Tommi Vatanen, Mikael Kuusela, Eric Malmi, Tapani Raiko, Timo Aaltonen and Yoshikazu Nagai Name of the publication Fixed-Background EM Algorithm for Semi-Supervised Anomaly Detection Publisher School of Science Unit Department of Information and Computer Science Series Aalto University publication series SCIENCE + TECHNOLOGY 2...

Unsupervised and Semi-supervised 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 through our LSTM based structure and obtain fixed length sequences. We then find a decision function for our anomaly detectors based on the One Class Support Vector Machines (...

Semi-supervised Statistical Approach for Network Anomaly Detection

Intrusion Detection Systems (IDS) have become a very important defense measure against security threats. In recent years, computer networks are widely deployed for critical and complex systems, which make them more vulnerable to network attacks. In this paper, we propose a two-stage Semi-supervised Statistical approach for Anomaly Detection (SSAD). The first stage of SSAD aims to build a probab...

Toward Supervised Anomaly Detection

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that...

Dissolved Air Flotation in Industrial Wastewater Treatment