602 research outputs found
A Survey on Explainable Anomaly Detection
In the past two decades, most research on anomaly detection has focused on
improving the accuracy of the detection, while largely ignoring the
explainability of the corresponding methods and thus leaving the explanation of
outcomes to practitioners. As anomaly detection algorithms are increasingly
used in safety-critical domains, providing explanations for the high-stakes
decisions made in those domains has become an ethical and regulatory
requirement. Therefore, this work provides a comprehensive and structured
survey on state-of-the-art explainable anomaly detection techniques. We propose
a taxonomy based on the main aspects that characterize each explainable anomaly
detection technique, aiming to help practitioners and researchers find the
explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from
Data (TKDD) for publication (preprint version
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
Generative Adversarial Networks (GANs) have been used in many different
applications to generate realistic synthetic data. We introduce a novel GAN
with Autoencoder (GAN-AE) architecture to generate synthetic samples for
variable length, multi-feature sequence datasets. In this model, we develop a
GAN architecture with an additional autoencoder component, where recurrent
neural networks (RNNs) are used for each component of the model in order to
generate synthetic data to improve classification accuracy for a highly
imbalanced medical device dataset. In addition to the medical device dataset,
we also evaluate the GAN-AE performance on two additional datasets and
demonstrate the application of GAN-AE to a sequence-to-sequence task where both
synthetic sequence inputs and sequence outputs must be generated. To evaluate
the quality of the synthetic data, we train encoder-decoder models both with
and without the synthetic data and compare the classification model
performance. We show that a model trained with GAN-AE generated synthetic data
outperforms models trained with synthetic data generated both with standard
oversampling techniques such as SMOTE and Autoencoders as well as with state of
the art GAN-based models
Digital Twin of the Radio Environment: A Novel Approach for Anomaly Detection in Wireless Networks
The increasing relevance of resilience in wireless connectivity for Industry
4.0 stems from the growing complexity and interconnectivity of industrial
systems, where a single point of failure can disrupt the entire network,
leading to significant downtime and productivity losses. It is thus essential
to constantly monitor the network and identify any anomaly such as a jammer.
Hereby, technologies envisioned to be integrated in 6G, in particular joint
communications and sensing (JCAS) and accurate indoor positioning of
transmitters, open up the possibility to build a digital twin (DT) of the radio
environment. This paper proposes a new approach for anomaly detection in
wireless networks enabled by such a DT which allows to integrate contextual
information on the network in the anomaly detection procedure. The basic
approach is thereby to compare expected received signal strengths (RSSs) from
the DT with measurements done by distributed sensing units (SUs). Employing
simulations, different algorithms are compared regarding their ability to infer
from the comparison on the presence or absence of an anomaly, particular a
jammer. Overall, the feasibility of anomaly detection using the proposed
approach is demonstrated which integrates in the ongoing research on employing
DTs for comprehensive monitoring of wireless networks.Comment: 6 pages, 4 figure
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
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