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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NS, NWS (Joint) |
2024-01-25 13:00 |
Hiroshima | Higashisenda Campus, HiroshimaUniversity + Online (Primary: On-site, Secondary: Online) |
[Encouragement Talk]
Performance evaluation of network anomaly detection and failure scale estimation method. Naoya Ogawa, Ryoichi Kawahara (Toyo Univ.)  NS2023-159 |
In this paper, we evaluate the performance of the method proposed in "Network anomaly detection and failure scale estima... [more] | NS2023-159 pp.1-6 |
| LOIS, ICM | 2024-01-26 10:10 |
Nagasaki | Nagasaki Prefectural Art Museum (Primary: On-site, Secondary: Online) |
Service Failure Detection Focusing on Simultaneous Increase in Various Types of Connection Retries in User Traffic Naoki Hayashi, Fumio Katayama, Naoki Tateishi, Osamu Okino, Mitsuho Tahara (NTT) ICM2023-34 LOIS2023-38 |
Many communication services are provided in carrier networks, and these communication services are realized by various e... [more] | ICM2023-34 LOIS2023-38 pp.33-38 |
| IBISML | 2023-12-20 14:55 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Anomaly Detection by One-class Convolution Extreme Learning Machine Using Multiple Kernel Yuta Okami, Takuya Kitamura (NIT, Toyama College) IBISML2023-31 |
In this paper, we propose a one-class convolutional extreme learning machine using multiple kernel. In this method, for ... [more] | IBISML2023-31 pp.7-12 |
| IBISML | 2023-12-20 16:25 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Anomaly detection by deep support data descriptions with pseudo-anomaly data Shuta Tsuchio, Takuya Kitamura (NIT, Toyama college) IBISML2023-34 |
This paper presents deep support vector data description (DSVDD) with pseudo-anomaly data that generated by generative m... [more] | IBISML2023-34 pp.25-30 |
| NS, RCS (Joint) |
2023-12-14 16:00 |
Fukuoka | Kyushu Institute of Technology Tobata campus, and Online (Primary: On-site, Secondary: Online) |
Evaluation of HttpRequest anomaly detection model using fastText and convolutional autoencoder Haruta Yamada, Ryoichi Kawahara (Toyo Univ.) NS2023-133 |
With the advent of the Internet and its close connection to people's lives, web applications are becoming increasingly i... [more] | NS2023-133 pp.42-47 |
| EMCJ | 2023-11-24 13:00 |
Tokyo | Kikai-Shinko-Kaikan (Primary: On-site, Secondary: Online) |
Detection Sensitivity Improvement by Removing Reflected Pulse Influence from a Bus Type Network Branch in Sequence Time Domain Reflectometry Takashi Kakiuchi, Kengo iokibe, Yoshitaka Toyota (Okayama Univ) EMCJ2023-73 |
Using the fact that cross-correlation between transmitted and reflected pulses in Sequence Time Domain Reflectometry can... [more] | EMCJ2023-73 pp.7-12 |
| IA | 2023-11-22 16:25 |
Aomori | Aomori Prefecture Tourist Center ASPM (Aomori) (Primary: On-site, Secondary: Online) |
Improving the accuracy of flow prediction and anomaly detection in GAMPAL, a general-purpose anomaly detection mechanism for Internet traffic Taku Wakui (Keio Univ./Hitachi), Fumio Teraoka (Keio Univ.), Takao Kondo (Hokkaido Univ./Keio Univ.) IA2023-41 |
The authors propose a general-purpose anomaly detection mechanism using Prefix Aggregate without Labeled data (GAMPAL) f... [more] | IA2023-41 pp.33-40 |
| ICM, NS, CQ, NV (Joint) |
2023-11-22 09:25 |
Ehime | Ehime Prefecture Gender Equality Center (Primary: On-site, Secondary: Online) |
A Study on Transfer of Decision Tree for Operation of Future Managed Networks Takaaki Moriya, Takashi Mukai, Manabu Nishio, Ai Tsunoda, Ken Kanishima (NTT) ICM2023-26 |
When we build a new managed network, we need knowledge to solve various failures that will be occurred in the network. H... [more] | ICM2023-26 pp.20-25 |
| KBSE, SC | 2023-11-18 13:55 |
Miyagi | Sento Kaikan | Improving the accuracy of machine learning based HDD failure prediction in on-premises storage Taku Wakui, Mineyoshi Masuda, Tomoya Oota (Hitachi) KBSE2023-48 SC2023-31 |
The HDD (Hard Disk Drive) failure predictive detection technology is one of the functions for managing on-premises stora... [more] | KBSE2023-48 SC2023-31 pp.81-86 |
| MIKA (3rd) |
2023-10-10 15:35 |
Okinawa | Okinawa Jichikaikan (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Anomaly Detection using HDBSCAN and Deep SVDD Yusuke Noji, Tomotaka Kimura, Jun Cheng (Doshisha Univ.) |
In this presentation, we examine an anomaly detection method using Deep SVDD (Support Vector Data Description), a type o... [more] | |
| IA | 2023-09-22 14:00 |
Hokkaido | Hokkaido Univeristy (Primary: On-site, Secondary: Online) |
Investigation of Acoustic Feature Estimation Based on Periodicity of Mechanical Equipment for Remote Anomaly Detection System Rio Shigyo, Daiki Nobayashi, Kazuya Tsukamoto, Mitsunori Mizumachi, Takeshi Ikenaga (KIT) IA2023-28 |
Status checks of mechanical equipment are typically being conducted by workers (manpower). However, since the check timi... [more] | IA2023-28 pp.105-108 |
| NS, IN, CS, NV (Joint) |
2023-09-08 09:30 |
Miyagi | Tohoku University (Primary: On-site, Secondary: Online) |
Network anomaly detection and failure scale estimation method Naoya Ogawa, Ryoichi Kawahara (Toyo Univ.) NS2023-57 |
In this paper, we propose a network anomaly detection and failure scale estimation method using AI. For anomaly detectio... [more] | NS2023-57 pp.32-37 |
| NS | 2023-04-14 11:40 |
Fukushima | Nihon University, Koriyama Campus + Online (Primary: On-site, Secondary: Online) |
Network Anomaly Detection through Variable Granularity Traffic Analysis Shohei Kamamura, Yuya Takeda (Seikei Univ.), Yuki Takei, Masato Nishiguchi, Yuhei Hayashi, Takayuki Fujiwara (NTT) NS2023-9 |
In the Society 5.0, it is important to accurately measure and analyze the communication traffic flow in wide-area IP net... [more] | NS2023-9 pp.44-49 |
| CCS | 2023-03-26 10:35 |
Hokkaido | RUSUTSU RESORT | Acquisition of physical kinetics of machines by reservoir computing Sena Kojima, Koki Minagawa, Taisei Saito, Tetsuya Asai (Hokkaido Univ.) CCS2022-67 |
This report focuses on an anomaly detection application of a machine’s dynamical system using reservoir computing. We pr... [more] | CCS2022-67 pp.25-30 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-15 11:00 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
Evaluating the Efficiency of Anomaly Detection Methods for Temporal Networks Using the Graph Spectrum Masataka Nagao, Eriko Segawa, Yusuke Sakumoto (Kwansei Gakuin Univ.) CQ2022-83 |
LAD (Laplacian Anomaly Detection) is a method for detecting anomalies in dynamic networks using the eigenvalues (the gra... [more] | CQ2022-83 pp.19-24 |
| RCC, ISEC, IT, WBS | 2023-03-15 09:30 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
Networks anomaly detection by VAE based on features extracted by CNN Higashihata Kazuki (Osaka Prefecture Univ.), Aoki Shigeki, Miyamoto Takao (Osaka Metropolitan Univ.) IT2022-111 ISEC2022-90 WBS2022-108 RCC2022-108 |
Anomaly-based IDS, one of the intrusion detection systems (IDS), can detect unknown anomalies, but there is a problem of... [more] | IT2022-111 ISEC2022-90 WBS2022-108 RCC2022-108 pp.269-276 |
| R | 2023-03-10 13:50 |
Hiroshima | (Primary: On-site, Secondary: Online) |
Failure Sign Detection by State Path Analysis for Fare Collection System
-- Evaluation by Sequential Pattern Mining with Mechatronics Knowledge -- Ken Ueno, Misato Ishikawa, Yuko Kobayashi, Takamitsu Sunaoshi (Toshiba), Kiyoku Endo (Toshiba Automation Systems Service) R2022-50 |
To detect failure sign on Fare Collection System (FCS) which has low failure rate accurately, we need the mechatronics k... [more] | R2022-50 pp.13-18 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 10:35 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Investigation of Appearance Inspection Method Considering the Number of Corresponding Local Patches Katsuhisa Kitaguchi, Yohei Nishizaki, Mamoru Saito (ORIST) PRMU2022-74 IBISML2022-81 |
There has been a great deal of research on appearance inspection using deep learning, which learns only from normal imag... [more] | PRMU2022-74 IBISML2022-81 pp.88-92 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 16:20 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Classifying Cable Tendency with Semantic Segmentation by Utilizing Real and Simulated RGB Data Pei-Chun Chien, Powei Liao, Eiji Fukuzawa, Jun Ohya (Waseda Univ.) PRMU2022-117 IBISML2022-124 |
Cable tendency is the potential shape or characteristic that a cable may possess while being manipulated during automate... [more] | PRMU2022-117 IBISML2022-124 pp.311-318 |
| IN, NS (Joint) |
2023-03-03 11:00 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
Unidentified Floating Object Detecting Method in Maritime Environment using Efficient GAN Hiromu Habuka, Kohta Ohshima (TUMSAT) NS2022-230 |
(To be available after the conference date) [more] | NS2022-230 pp.362-367 |
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