no code implementations • 24 Mar 2020 • Kengo Tajiri, Yasuhiro Ikeda, Yuusuke Nakano, Keishiro Watanabe
We present the results from experiments involving benchmark data and real log data, which indicate that our method using divided models does not decrease anomaly detection accuracy and a model for anomaly detection can be divided to continue monitoring a network state even if some the log data change.
no code implementations • 24 Mar 2020 • Hiroki Ikeuchi, Akio Watanabe, Tsutomu Hirao, Makoto Morishita, Masaaki Nishino, Yoichi Matsuo, Keishiro Watanabe
With the increase in scale and complexity of ICT systems, their operation increasingly requires automatic recovery from failures.
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.
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.