An incident analysis system NICTER and its analysis engines based on data mining techniques

Daisuke Inoue, Katsunari Yoshioka, Masashi Eto, Masaya Yamagata, Eisuke Nishino, Junnichi Takeuchi, Kazuya Ohkouchi, Koji Nakao

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

19 被引用数 (Scopus)

抄録

Malwares are spread all over cyberspace and often lead to serious security incidents. To grasp the present trends of malware activities, there are a number of ongoing network monitoring projects that collect large amount of data such as network traffic and IDS logs. These data need to be analyzed in depth since they potentially contain critical symptoms, such as an outbreak of new malware, a stealthy activity of botnet and a new type of attack on unknown vulnerability, etc. We have been developing the Network Incident analysis Center for Tactical Emergency Response (NICTER), which monitors a wide range of networks in real-time. The NICTER deploys several analysis engines taking advantage of data mining techniques in order to analyze the monitored traffics. This paper describes a brief overview of the NICTER, and its data mining based analysis engines, such as Change Point Detector (CPD), Self-Organizing Map analyzer (SOM analyzer) and Incident Forecast engine (IF).

本文言語英語
ホスト出版物のタイトルAdvances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
ページ579-586
ページ数8
PART 1
DOI
出版ステータス出版済み - 9 21 2009
イベント15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland, ニュージ―ランド
継続期間: 11 25 200811 28 2008

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
5506 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他15th International Conference on Neuro-Information Processing, ICONIP 2008
Countryニュージ―ランド
CityAuckland
Period11/25/0811/28/08

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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