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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-09 09:55 |
Online | Online (Zoom) | Active intrusion detection method for IoT devices Takahiro Ohtani, Ryo Yamamoto, Satoshi Ohzahata (UEC)  CQ2021-101 |
In recent years, the threat of attacks against IoT (Internet of Things) devices has become apparent with the rapid sprea... [more] | CQ2021-101 pp.5-10 |
| EMM | 2022-03-07 17:00 |
Online | (Primary: Online, Secondary: On-site) (Primary: Online, Secondary: On-site) |
Extention of robust image classification system with Adversarial Example Detectors Miki Tanaka, Takayuki Osakabe, Hitoshi Kiya (Tokyo Metro. Univ.) EMM2021-105 |
In image classification with deep learning, there is a risk that an attacker can intentionally manipulate the prediction... [more] | EMM2021-105 pp.76-80 |
| VLD, HWS [detail] | 2022-03-08 10:20 |
Online | Online | Evaluation of leakage-based LR-PUF's resistance to machine learning attacks Tomoaki Oikawa, Kimiyoshi Usami (SIT) VLD2021-93 HWS2021-70 |
One of the LSI individual identification technologies is PUF (Physically Unclonable Function), which utilizes the physic... [more] | VLD2021-93 HWS2021-70 pp.93-98 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 10:10 |
Online | Online | A Multilayer Perceptron Training Accelerator using Systolic Array Takeshi Senoo, Akira Jinguji, Ryosuke Kuramochi, Hiroki Nakahara (Toyko Tech) VLD2021-23 ICD2021-33 DC2021-29 RECONF2021-31 |
Neural networks are being used in various applications, and the demand for fast training with large amounts of data is e... [more] | VLD2021-23 ICD2021-33 DC2021-29 RECONF2021-31 pp.37-42 |
| RISING (3rd) |
2021-11-16 09:30 |
Tokyo | (Primary: On-site, Secondary: Online) |
On Attack Pattern Classification in IoT Networks for Network Intrusion Detection Systems Jesse Atuhurra, Takanori Hara (NAIST), Yuanyu Zhang (Xidian Univ.), Shoji Kasahara (NAIST) |
With the proliferation of IoT devices, IoT security problems arise. To protect heterogeneous connected devices in IoT ne... [more] | |
| IN, NS, CS, NV (Joint) |
2021-09-09 14:05 |
Online | Online | A Machine Learning Based Network Intrusion Detection System with Appling Different Algorithms in Multiple Stages Seiichi Sasa, Hiroyuki Suzuki, Akio Koyama (Yamagata Univ.) NS2021-63 |
In recent years, the rapid development of Information and Communication Technology (ICT) has led to the provision of a w... [more] | NS2021-63 pp.36-41 |
| ISEC | 2021-05-19 15:30 |
Online | Online | [Invited Talk]
Simple Electromagnetic Analysis Against Activation Functions of Deep Neural Networks (from AIHWS 2020) Go Takatoi, Takeshi Sugawara, Kazuo Sakiyama (UEC), Yuko Hara-Azumi (Tokyo Tech), Yang Li (UEC) ISEC2021-9 |
This invited abstract is based on the papers [1] and [2]. There are physical attacks such as side-channel attacks that a... [more] | ISEC2021-9 p.34 |
| ICSS, IPSJ-SPT | 2021-03-01 10:25 |
Online | Online | Construction of Vulnerability Evaluation System with Machine Learning Methods Ryu Watanabe, Takashi Matsunaka, Ayumu Kubota (KDDIR), Junpei Urakawa (KDS/KDDIR) ICSS2020-29 |
Recently, the cyberattacks aimed at software vulnerabilities are more popular and powerful. Therefore, various incidents... [more] | ICSS2020-29 pp.19-24 |
| AI | 2021-02-12 16:30 |
Online | Online | A Defense Method for Machine Learning Poisoning Attacks in IoT Environments Considering the Removal Priority of Poisonous Data Tomoki Chiba, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga (UEC) AI2020-36 |
In recent years, machine learning has been attracting attention for its potential to further enrich people's lives. Howe... [more] | AI2020-36 pp.73-78 |
| NS, NWS (Joint) |
2021-01-22 14:20 |
Online | Online | An Aggregation Approach for Improving Network Scan Failure and Delay Estimation of IoT Wireless Equipment Babatunde Ojetunde, Kenta Suzuki, Kazuto Yano, Yoshinori Suzuki (ATR) NS2020-118 |
Recently we proposed a method for identifying the network scan response state that is needed to estimate the cause of ne... [more] | NS2020-118 pp.43-52 |
| ICSS | 2020-11-26 14:00 |
Online | Online | Malware detection for IoT devices using whitelist and Isolation Forest Masataka Nakahara, Norihiro Okui, Yasuaki Kobayashi, Yutaka Miyake (KDDI Research) ICSS2020-20 |
As the number of IoT (Internet of Things) devices increases, the countermeasures against cyberattacks related to IoT dev... [more] | ICSS2020-20 pp.7-12 |
| SIS, ITE-BCT | 2020-10-01 13:00 |
Online | Online | Evaluation of linear dimensionality reduction methods considering visual information protection for privacy-preserving machine learning Masaki Kitayama, Nobutaka Ono, Hitoshi Kiya (Tokyo Metro. Univ.) SIS2020-13 |
In this paper, linear dimensionality reduction methods are evaluated in terms of difficulty in estimating the visual inf... [more] | SIS2020-13 pp.17-22 |
| ICM | 2020-03-03 10:00 |
Okinawa | Ohama Nobumoto Memorial Hall (Cancelled but technical report was issued) |
Automatic Accumulation of Learning Data on Learning-based Anomaly Detection Utilizing Communication Traffics Natsuki Fukazawa, Naoki Yoshida, Shingo Ata, Ikuo Oka (Osaka City Univ.) ICM2019-50 |
With the advancement and diversification of information infrastructure in recent years, the importance of network secur... [more] |
ICM2019-50 pp.49-54 |
| ISEC, SITE, LOIS | 2019-11-02 15:00 |
Osaka | Osaka Univ. | On Robustness of Machine-Learning-Based Malware Detection Wanjia Zheng (U. Tsukuba), Kazumasa Omote (U. Tsukuba/NICT) ISEC2019-83 SITE2019-77 LOIS2019-42 |
As the 2020 Tokyo Olympics are approaching, the possibility of being targeted by attackers has further increased in Japa... [more] | ISEC2019-83 SITE2019-77 LOIS2019-42 pp.133-140 |
| ICM, IPSJ-CSEC, IPSJ-IOT | 2019-05-24 09:25 |
Osaka | Feature Value for Low-Bandwidth L3, L4 DDoS Detection based on Number of 5-tuple Flows in 3-tuple Flow Yuhei Hayashi (NTT), Hikofumi Suzuki (Shindai), Takeaki Nishioka (NTT) ICM2019-5 |
Recently, new sophisticated attacks such as pulse-wave DDoS has been observed. The DDoS attack repeats short duration at... [more] | ICM2019-5 pp.65-70 |
|
| IT, ISEC, WBS | 2019-03-07 09:55 |
Tokyo | University of Electro-Communications | Exploring Malicious URL in Dark Web Using Tor Crawler Yuki Kawaguchi, Seiichi Ozawa (Kobe Univ.) IT2018-76 ISEC2018-82 WBS2018-77 |
In recent years, various web-based attacks such as Drive-by-Download attacks are becoming serious. To protect legitimate... [more] | IT2018-76 ISEC2018-82 WBS2018-77 pp.7-12 |
| IN, NS (Joint) |
2019-03-04 09:00 |
Okinawa | Okinawa Convention Center | Intrusion Detection System using semi-supervised learning with Adversarial Autoencoder Kazuki Hara, Kohei Shiomoto (Tokyo City Univ.) NS2018-193 |
In recent years the importance of intrusion detection system(IDS) is increasing. In particular, a method using machine l... [more] | NS2018-193 pp.1-6 |
| IN, NS (Joint) |
2019-03-05 14:30 |
Okinawa | Okinawa Convention Center | Proposal of real-time brute-force attack detection and blocking system using software switch Yusei Katsura, Hiroyuki Kimiyama, Tomoaki Tsutsumi, Naoki Yonezaki (Tokyo Denki Univ.), Junki Ichikawa (NTT), Mitsuru Maruyama (Kanagawa Instiute of Technology) NS2018-272 |
Recently, both number and scale of cyber attacks by using a large scale BotNet are increasing year by year. The BotNet e... [more] | NS2018-272 pp.461-464 |
| HWS, VLD | 2019-03-01 15:20 |
Okinawa | Okinawa Ken Seinen Kaikan | An Attack with Linear Model Against Improved Arbiter PUF Susumu Matsumi, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) VLD2018-132 HWS2018-95 |
Imitations of electronic parts are distributed to the market, which is a serious problem. PUFs have attracted attention ... [more] | VLD2018-132 HWS2018-95 pp.231-236 |
| HWS, VLD | 2019-03-01 15:45 |
Okinawa | Okinawa Ken Seinen Kaikan | On Machine Learning Attack Tolerance for PUF-based Device Authentication System Tomoki Iizuka (UTokyo), Yasuhiro Ogasahara, Toshihiro Katashita, Yohei Hori (AIST), Hiromitsu Awano (Osaka Univ.), Makoto Ikeda (UTokyo) VLD2018-133 HWS2018-96 |
Double-Arbiter PUF (DAPUF) and PL-PUF are known to be highly resistant to machine learning attacks. In this paper, we p... [more] |
VLD2018-133 HWS2018-96 pp.237-242 |
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