研究者詳細 - 小林　諭

論文 - 小林　諭

分割表示 31 件中 21 - 31 件目／全件表示 >>

Causal analysis of network logs with layered protocols and topology knowledge 査読

Satoru Kobayashi, Kazuki Otomo, Kensuke Fukuda

Proceedings of CNSM 2019 2019年10月

Latent Variable based Anomaly Detection in Network System Logs 査読

Kazuki Otomo, Satoru Kobayashi, Kensuke Fukuda, Hiroshi Esaki

IEICE Transactions on Information and Systems E102-D ( 9 ) 1644 - 1652 2019年9月

ARP Request Trend Fitting for Detecting Malicious Activity in LAN 査読

Kai Matsufuji, Satoru Kobayashi, Hiroshi Esaki, Hideya Ochiai

Proceedings of IMCOM 2019 89 - 96 2019年1月

Finding Anomalies in Network System Logs with Latent Variables 査読

Kazuki Otomo, Satoru Kobayashi, Kensuke Fukuda, Hiroshi Esaki

Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (BigDAMA) 8 - 14 2018年8月

Mining Causality of Network Events in Log Data 査読

Satoru Kobayashi, Kazuki Otomo, Kensuke Fukuda, Hiroshi Esaki

IEEE Transactions on Network and Service Management 15 ( 1 ) 53 - 67 2018年3月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Institute of Electrical and Electronics Engineers Inc.

Network log messages (e.g., syslog) are expected to be valuable and useful information to detect unexpected or anomalous behavior in large scale networks. However, because of the huge amount of system log data collected in daily operation, it is not easy to extract pinpoint system failures or to identify their causes. In this paper, we propose a method for extracting the pinpoint failures and identifying their causes from network syslog data. The methodology proposed in this paper relies on causal inference that reconstructs causality of network events from a set of time series of events. Causal inference can filter out accidentally correlated events, thus it outputs more plausible causal events than traditional cross-correlation-based approaches can. We apply our method to 15 months' worth of network syslog data obtained from a nationwide academic network in Japan. The proposed method significantly reduces the number of pseudo correlated events compared with the traditional methods. Also, through three case studies and comparison with trouble ticket data, we demonstrate the effectiveness of the proposed method for practical network operation.

DOI： 10.1109/TNSM.2017.2778096

Scopus

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Analyzing Burstness and Casuality of System logs 査読

Kazuki Otomo, Satoru Kobayashi, Kensuke Fukuda, Hiroshi Esaki

ACM SIGCOMM CoNEXT 2017 Student Workshop 2017年12月

An Analysis of Burstiness and Causality of System Logs 査読

Kazuki Otomo, Satoru Kobayashi, Kensuke Fukuda, Hiroshi Esaki

Proceedings of AINTEC 2017 2017年11月

Mining causes of network events in log data with causal inference 査読

Satoru Kobayashi, Kensuke Fukuda, Hiroshi Esaki

Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management 45 - 53 2017年7月

担当区分：筆頭著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Institute of Electrical and Electronics Engineers Inc.

Network log message (e.g., syslog) is valuable information to detect unexpected or anomalous behavior in a large scale network. However, pinpointing failures and their causes is not an easy problem because of a huge amount of system log data in daily operation. In this study, we propose a method extracting failures and their causes from network syslog data. The main idea of the method relies on causal inference that reconstructs causality of network events from a set of the time series of events. Causal inference allows us to reduce the number of correlated events by chance, thus it outputs more plausible causal events than a traditional cross-correlation based approach. We apply our method to 15 months network syslog data obtained in a nation-wide academic network in Japan. Our method significantly reduces the number of pseudo correlated events compared with the traditional method. Also, through two case studies and comparison with trouble ticket data, we demonstrate the effectiveness of our method for network operation.

DOI： 10.23919/INM.2017.7987263

Scopus

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Application of Change Point Detection to System Logs for Fault Detection 査読

ACM SIGCOMM CoNEXT 2016 Student Workshop (Poster Session) 2016年12月

Causation mining in network logs 査読

Satoru Kobayashi, Kensuke Fukuda, Hiroshi Esaki

ACM SIGCOMM CoNEXT 2016 Student Workshop 2016年12月

Towards an NLP-based log template generation algorithm for system log analysis 査読

Satoru Kobayashi, Kensuke Fukuda, Hiroshi Esaki

Proceedings of CFI 2014 2014年6月

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