Statistical approaches for semi-supervised anomaly detection in machining

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...

Statistical Approaches for Network Anomaly Detection

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...

Moving dispersion method for statistical anomaly detection in intrusion detection systems

A unified method for statistical anomaly detection in intrusion detection systems is theoretically introduced. It is based on estimating a dispersion measure of numerical or symbolic data on successive moving windows in time and finding the times when a relative change of the dispersion measure is significant. Appropriate dispersion measures, relative differences, moving windows, as well as tec...

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 (...