Keisuke Ishibashi

Yasuhiro Ikeda, Keisuke Ishibashi, Yuusuke Nakano et al.

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.

Yasuhiro Ikeda, Kengo Tajiri, Yuusuke Nakano et al.

Anomaly detection using dimensionality reduction has been an essential technique for monitoring multidimensional data. Although deep learning-based methods have been well studied for their remarkable detection performance, their interpretability is still a problem. In this paper, we propose a novel algorithm for estimating the dimensions contributing to the detected anomalies by using variational autoencoders (VAEs). Our algorithm is based on an approximative probabilistic model that considers the existence of anomalies in the data, and by maximizing the log-likelihood, we estimate which dimensions contribute to determining data as an anomaly. The experiments results with benchmark datasets show that our algorithm extracts the contributing dimensions more accurately than baseline methods.