  Automated anomaly detection is essential for managing information and
communications technology (ICT) systems to maintain reliable services with
minimum burden on operators. For detecting varying and continually emerging
anomalies as differences from normal states, learning normal relationships
inherent among cross-domain data monitored from ICT systems is essential.
Deep-learning-based anomaly detection using an autoencoder (AE) is therefore
promising for such complicated learning; however, its interpretation is still
problematic. Since the dimensions of the input data contributing to the
detected anomaly are not directly indicated in an AE, they are not suitable for
localizing anomalies in large ICT systems composed of a huge amount of
equipment. We propose an algorithm using sparse optimization for estimating
contributing dimensions to anomalies detected with AEs. We also propose a
multimodal AE (MAE) for effectively learning the relationships among
cross-domain data, which can induce nonlinearity and differences in
learnability among data types. We evaluated our algorithms with several
datasets including real measured data in comparison with conventional
algorithms and confirmed the superiority of our estimation algorithm in
specifying contributing dimensions of anomalous data and our MAE in detecting
anomalies in cross-domain data.
