Zobrazeno 1 - 10 of 12 pro vyhledávání: '"Jumpei Shimamura"'
2
Real-Time Detection of Malware Activities by Analyzing Darknet Traffic Using Graphical Lasso
Autor: Jun'ichi Takeuchi, Takeshi Takahashi, Koji Nakao, Masanori Kawakita, Daisuke Inoue, Jumpei Shimamura, Chansu Han
Publikováno v: TrustCom/BigDataSE
Recent malware evolutions have rendered cyberspace less secure, and we are currently witnessing an increasing number of severe security incidents. To minimize the impact of malware activities, it is important to detect them promptly and precisely. We
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ab5fa7b4a970e10a90162922e576b67c
https://doi.org/10.1109/trustcom/bigdatase.2019.00028
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3
Real-time botnet detection using nonnegative tucker decomposition
Autor: Jumpei Shimamura, Noboru Murata, Daisuke Inoue, Hideaki Kanehara, Takeshi Takahashi, Yuma Murakami
Publikováno v: SAC
This study focuses on darknet traffic analysis and applies tensor factorization in order to detect coordinated group activities, such as a botnet. Tensor factorization is a powerful tool for extracting co-occurrence patterns that is highly interpreta
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::57eb6a4f36607321ad02014af1731aca
https://doi.org/10.1145/3297280.3297415
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4
Large-Scale Monitoring for Cyber Attacks by Using Cluster Information on Darknet Traffic Features
Autor: Jun Kitazono, Hironori Nishikaze, Jumpei Shimamura, Seiichi Ozawa, Tao Ban, Junji Nakazato
Publikováno v: INNS Conference on Big Data
This paper presents a machine learning approach to large-scale monitoring for malicious activities on Internet. In the proposed system, network packets sent from a subnet to a darknet (i.e., a set of unused IPs) are collected, and they are transforme
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::440fb6e6f05367b91586bc3e40620417
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5
An Online Malicious Spam Email Detection System Using Resource Allocating Network with Locality Sensitive Hashing
Autor: Siti-Hajar-Aminah Ali, Tao Ban, Seiichi Ozawa, Junji Nakazato, Jumpei Shimamura
Publikováno v: Journal of Intelligent Learning Systems and Applications. :42-57
In this paper, we propose a new online system that can quickly detect malicious spam emails and adapt to the changes in the email contents and the Uniform Resource Locator (URL) links leading to malicious websites by updating the system daily. We int
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3e0431d022b57367fcfed48ee02b9fbc
https://doi.org/10.4236/jilsa.2015.72005
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6
Detection of Botnet Activities Through the Lens of a Large-Scale Darknet
Autor: Daisuke Inoue, Lei Zhu, Jumpei Shimamura, Tao Ban, Shaoning Pang, Koji Nakao
Publikováno v: Neural Information Processing ISBN: 9783319701387
ICONIP (5)
The growing cyber-threats from botnets compel us to devise proper countermeasures to detect infected hosts in an efficient and timely manner. In this paper, botnet-host identification is approached from a new perspective: by exploring the temporal co
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7a601ef4e09e63a4d313f51cab68c7b6
https://doi.org/10.1007/978-3-319-70139-4_45
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7
A neural network model for detecting DDoS attacks using darknet traffic features
Autor: Junji Nakazato, Siti Hajar Aminah Ali, Tao Ban, Seiichi Ozawa, Jumpei Shimamura
Publikováno v: IJCNN
This paper presents a fast and large-scale monitoring system for detecting one of the major cyber-attacks, Distributed Denial of Service (DDoS). The proposed system monitors the packet traffic on a subnet of unused IPs called darknet. Almost all dark
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f1695fdadc34428ed1a2992f2a2eabd3
https://doi.org/10.1109/ijcnn.2016.7727577
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8
An autonomous online malicious spam email detection system using extended RBF network
Autor: Tao Ban, Jumpei Shimamura, Siti-Hajar-Aminah Ali, Seiichi Ozawa, Junji Nakazato
Publikováno v: IJCNN
In this paper, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from do
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::72cba05bcc0bea353c3498a457d014d5
https://doi.org/10.1109/ijcnn.2015.7280826
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10
Adaptive DDoS-Event Detection from Big Darknet Traffic Data
Autor: Nobuaki Furutani, Tao Ban, Jun Kitazono, Junji Nakazato, Jumpei Shimamura, Seiichi Ozawa
Publikováno v: Neural Information Processing ISBN: 9783319265605
ICONIP (4)
This paper presents an adaptive large-scale monitoring system to detect Distributed Denial of Service (DDoS) attacks whose backscatter packets are observed on the darknet (i.e., unused IP space). To classify DDoS backscatter, 17 features of darknet t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b052abc915c3d826010909fe233a20cc
https://doi.org/10.1007/978-3-319-26561-2_45
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