23,512 research outputs found
Multi-level anomaly detection in industrial control systems via package signatures and LSTM networks
We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques
Anomaly Detection in Electrocardiogram Readings with Stacked LSTM Networks
Real-world anomaly detection for time series is still a challenging task. This is especially true for periodic or quasi-periodic time series since automated approaches have to learn long-term correlations before they are able to detect anomalies. Electrocardiography (ECG) time series, a prominent real-world example of quasi-periodic signals, are investigated in this work. Anomaly detection algorithms often have the additional goal to identify anomalies in an unsupervised manner. In this paper we present an unsupervised time series anomaly detection algorithm. It learns with recurrent Long Short-Term Memory (LSTM) networks to predict the normal time series behavior. The prediction error on several prediction horizons is used to build a statistical model of normal behavior. We propose new methods that are essential for a successful model-building process and for a high signal-to-noise-ratio. We apply our method to the well-known MIT-BIH ECG data set and present first results. We obtain a good recall of anomalies while having a very low false alarm rate (FPR) in a fully unsupervised procedure. We compare also with other anomaly detectors (NuPic, ADVec) from the state-of-the-art.Algorithms and the Foundations of Software technolog
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout
Recently, there has been a significant amount of interest in satellite
telemetry anomaly detection (AD) using neural networks (NN). For AD purposes,
the current approaches focus on either forecasting or reconstruction of the
time series, and they cannot measure the level of reliability or the
probability of correct detection. Although the Bayesian neural network
(BNN)-based approaches are well known for time series uncertainty estimation,
they are computationally intractable. In this paper, we present a tractable
approximation for BNN based on the Monte Carlo (MC) dropout method for
capturing the uncertainty in the satellite telemetry time series, without
sacrificing accuracy. For time series forecasting, we employ an NN, which
consists of several Long Short-Term Memory (LSTM) layers followed by various
dense layers. We employ the MC dropout inside each LSTM layer and before the
dense layers for uncertainty estimation. With the proposed uncertainty region
and by utilizing a post-processing filter, we can effectively capture the
anomaly points. Numerical results show that our proposed time series AD
approach outperforms the existing methods from both prediction accuracy and AD
perspectives
Online learning of windmill time series using Long Short-term Cognitive Networks
Forecasting windmill time series is often the basis of other processes such
as anomaly detection, health monitoring, or maintenance scheduling. The amount
of data generated on windmill farms makes online learning the most viable
strategy to follow. Such settings require retraining the model each time a new
batch of data is available. However, update the model with the new information
is often very expensive to perform using traditional Recurrent Neural Networks
(RNNs). In this paper, we use Long Short-term Cognitive Networks (LSTCNs) to
forecast windmill time series in online settings. These recently introduced
neural systems consist of chained Short-term Cognitive Network blocks, each
processing a temporal data chunk. The learning algorithm of these blocks is
based on a very fast, deterministic learning rule that makes LSTCNs suitable
for online learning tasks. The numerical simulations using a case study with
four windmills showed that our approach reported the lowest forecasting errors
with respect to a simple RNN, a Long Short-term Memory, a Gated Recurrent Unit,
and a Hidden Markov Model. What is perhaps more important is that the LSTCN
approach is significantly faster than these state-of-the-art models
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