Extendable NFV-Integrated Control Method Using Reinforcement Learning

no code implementations • 19 Dec 2019 • Akito Suzuki, Ryoichi Kawahara, Masahiro Kobayashi, Shigeaki Harada, Yousuke Takahashi, Keisuke Ishibashi

The NFV control method should also be extendable for adding new metrics or changing the combination of metrics.

reinforcement-learning Reinforcement Learning (RL)

Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization

no code implementations • 18 Dec 2018 • Yasuhiro Ikeda, Keisuke Ishibashi, Yuusuke Nakano, Keishiro Watanabe, Ryoichi Kawahara

Automated anomaly detection is essential for managing information and communications technology (ICT) systems to maintain reliable services with minimum burden on operators.

Anomaly Detection

Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders

no code implementations • 12 Nov 2018 • Yasuhiro Ikeda, Kengo Tajiri, Yuusuke Nakano, Keishiro Watanabe, Keisuke Ishibashi

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.

Anomaly Detection Dimensionality Reduction