AccessJP

Search

Menu

Study at TCU

Reseacher

Name SHIOMOTO Kohei
Official Title Professor
Affiliation Faculty of Intelligent Systems, Information Technology, Department of Information Technology
E-mail shiomoto@tcu.ac.jp
Web
  1. https://www.risys.gl.tcu.ac.jp/Main.php?action=profile&type=detail&tchCd=5002019
  2. https://www.linkedin.com/in/kohei-shiomoto-676bb5/
Profile Kohei Shiomoto is a Professor at Tokyo City University, Tokyo Japan. He had been engaged in R&D in the data communication industry for over 25 years since he joined NTT Laboratories in 1989. He has been active in the areas of network virtualization, data-mining for network management, traffic & QoE management since he joined Tokyo City University in 2017. He served as Guest Co-Editor for a series of special issues established in IEEE TNSM on Management of Softwareized Networks. He has served in various roles organizing IEEE ComSoc high profile conferences such as IEEE NOMS, IEEE IM, and IEEENetSoft.
Research Field(Keyword & Summary)
  1. Application of Machine Learning to Network Intrusion Detection Systems

    We investigate the performance of a semi-supervised learning NIDS based on Adversarial Autoencoder (AAE) proposed in our earlier work through a series of detailed experiments. We demonstrate the proposed AAEbased NIDS outperforms the conventional machine learningbased NIDS method. The proposed semi-supervised NIDS based on AAE achieves high performance while it reduces the required number of labeled data samples in the training dataset, which requires costly human-labor tasks.

  2. Application of Few-Short Learning to Sevice Assuarance of LTE Networks

    To mitigate the cost and time issues, we propose a method based on few-shot learning that uses Prototypical Networks algorithm to complement the eNodeB states analysis. Using a dataset from a live LTE network consists of thousand of eNodeB, our experiment results show that the proposed technique provides high performance while using a low number of labeled data.

  3. Application of Bayesian Model for Traffic Forecast

    We propose a Bayesian model to model the relationship between the population and the network and computing resource. The parameters that govern the Bayesian network is estimated from the measurement.

  4. Trajectory Data Mining for Spatio-Temporal Human Mobility

    We propose a method to grasp the behavior of the mobility in spatio-temporal by mining the trajectory data of the mobility obtained from the GPS data to predict the future mobility of the user from frequent patterns. We apply sequential pattern mining algorithms including PrefixSpan and BIDE to obtain frequent trajectory patterns from trajectory database.
Representative Papers
  1. S. Aoki, K. Shiomoto and C. L. Eng, "Few-Shot Learning and Self-Training for eNodeB Log Analysis for Service-Level Assurance in LTE Networks," in IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 2077-2089, Dec. 2020, doi: 10.1109/TNSM.2020.3032156.
  2. K. Hara and K. Shiomoto, "Intrusion Detection System using Semi-Supervised Learning with Adversarial Auto-encoder," NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 2020, pp. 1-8, doi: 10.1109/NOMS47738.2020.9110343.
  3. S. Aoki, K. Shiomoto, C. L. Eng and S. Backstad, "Few-shot Learning for eNodeB Performance Metric Analysis for Service Level Assurance in LTE Networks," NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 2020, pp. 1-4, doi: 10.1109/NOMS47738.2020.9110296.
  4. K. Hara, K. Shiomoto, C. L. Eng and S. Backstad, "Automatic eNodeB state management in LTE networks using Semi-Supervised Learning with Adversarial Autoencoder," 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR), Newark, NJ, USA, 2020, pp. 1-6, doi: 10.1109/HPSR48589.2020.9098982.
  5. K. Ishii et al., "First Demonstration of Automated Updates of Disaggregate Blades in Multi-Domain/Layer Optical Path Network," 2020 Optical Fiber Communications Conference and Exhibition (OFC), San Diego, CA, USA, 2020, pp. 1-3.
  6. K. Ishii et al., "Automatic Resource Mapping using Functional Block Based Disaggregation Model for ROADM Networks," 2020 Optical Fiber Communications Conference and Exhibition (OFC), San Diego, CA, USA, 2020, pp. 1-3.
  7. S. Nakamura et al., "First Demonstration of End-to-End Network Slicing with Transport Network Coordination and Edge Cloud Applications in 5G Era," 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC), Fukuoka, Japan, 2019, pp. 1-3, doi: 10.23919/PS.2019.8817850.
  8. Y. Tsukuda, M. Kosugi, K. Shiomoto, T. Morita and T. Hayashi, "Reducing Inconsistency between Software-Defined Networking Controllers," 2019 IEEE Conference on Network Softwarization (NetSoft), Paris, France, 2019, pp. 301-305, doi: 10.1109/NETSOFT.2019.8806639.
  9. S. Enami and K. Shiomoto, "Spatio-temporal human mobility prediction based on trajectory data mining for resource management in mobile communication networks," 2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR), Xi'An, China, 2019, pp. 1-6, doi: 10.1109/HPSR.2019.8808106.
Patent
  1. US Patent 6718326 (Packet classification search device and method)
  2. US Patent 5878029(Variable-bandwidth network)
  3. US Patent 6,639,897 (Communication network of linked nodes for selecting the shortest available route)
  4. US Patent 5892604(ATM switch)
Award IEICE Best Paper Awards (2019), EEE Communications Society CQR Chairman's Award for sustained technical contributions and promoting technical program activities of the CQR Technical Committee in IEEE ICC and GLOBECOM(2016)
Grant-in-Aid for Scientific Research Support: Japan Society for Promotion of Science (JSPS) https://nrid.nii.ac.jp/ja/nrid/1000000535750/
Research Grants/Projects including subsidies, donations, grants, etc. 19K11950, 17H07156, 20S0103(NII), 19S0105(NII)
Recruitment of research assistant(s) Yes (2 people)
Affiliated academic society (Membership type) IEEE (Senior Member), IEICE(Fellow), ACM (member), IPSJ(member)
Education Field (Undergraduate level) Computer Network, Cloud Computing, Network Algorithm
Education Field (Graduate level) Advanced Computer Network

Affiliation