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アイテム
SIP Flooding Attack Detection Using a Trust Model and Statistical Algorithms
https://ipsj.ixsq.nii.ac.jp/records/98504
https://ipsj.ixsq.nii.ac.jp/records/98504ec2375dc-b212-4e0c-af29-573f5d0c5860
名前 / ファイル | ライセンス | アクション |
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Copyright (c) 2014 by the Information Processing Society of Japan
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オープンアクセス |
Item type | Journal(1) | |||||||||||||
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公開日 | 2014-02-15 | |||||||||||||
タイトル | ||||||||||||||
タイトル | SIP Flooding Attack Detection Using a Trust Model and Statistical Algorithms | |||||||||||||
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言語 | en | |||||||||||||
タイトル | SIP Flooding Attack Detection Using a Trust Model and Statistical Algorithms | |||||||||||||
言語 | ||||||||||||||
言語 | eng | |||||||||||||
キーワード | ||||||||||||||
主題Scheme | Other | |||||||||||||
主題 | [特集:ネットワークサービスと分散処理] IMS, security, flooding attack, statistical analysis, trust | |||||||||||||
資源タイプ | ||||||||||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
資源タイプ | journal article | |||||||||||||
著者所属 | ||||||||||||||
Nara Institute of Science and Technology | ||||||||||||||
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Nara Institute of Science and Technology | ||||||||||||||
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Nara Institute of Science and Technology | ||||||||||||||
著者所属 | ||||||||||||||
Nara Institute of Science and Technology | ||||||||||||||
著者所属(英) | ||||||||||||||
en | ||||||||||||||
Nara Institute of Science and Technology | ||||||||||||||
著者所属(英) | ||||||||||||||
en | ||||||||||||||
Nara Institute of Science and Technology | ||||||||||||||
著者所属(英) | ||||||||||||||
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Nara Institute of Science and Technology | ||||||||||||||
著者所属(英) | ||||||||||||||
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Nara Institute of Science and Technology | ||||||||||||||
著者名 |
Noppawat, Chaisamran
× Noppawat, Chaisamran
× Takeshi, Okuda
× Youki, Kadobayashi
× Suguru, Yamaguchi
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著者名(英) |
Noppawat, Chaisamran
× Noppawat, Chaisamran
× Takeshi, Okuda
× Youki, Kadobayashi
× Suguru, Yamaguchi
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論文抄録 | ||||||||||||||
内容記述タイプ | Other | |||||||||||||
内容記述 | The IP Multimedia Subsystem (IMS) has been constantly evolving to meet the tremendous rise in popularity of mobile services and Internet applications. Since IMS uses Session Initiation Protocol as the main protocol to control a signal, it inherits numerous known security vulnerabilities. One of the most severe issues is the Denial of Service attack. To address this problem, we introduce an anomaly-based detection system using the Tanimoto distance to identify deviations in the traffic. A modified moving average is applied to compute an adaptive threshold. To overcome a drawback of the adaptive threshold method, we present a momentum oscillation indicator to detect a gradually increasing attack. Generally, anomaly-based detection systems trigger many alarms and most of them are false positives that impact the quality of the detection. Therefore, we first present a false positive reduction method by using a trust model. A reliable trust value is calculated through the call activities and the human behavior of each user. The system performance is evaluated by using a comprehensive synthetic dataset containing various malicious traffic patterns. The experimental results show that this system accurately identified attacks and has the flexibility to deal with many types of attack patterns with a low false alarm. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online) DOI http://dx.doi.org/10.2197/ipsjjip.22.118 ------------------------------ |
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論文抄録(英) | ||||||||||||||
内容記述タイプ | Other | |||||||||||||
内容記述 | The IP Multimedia Subsystem (IMS) has been constantly evolving to meet the tremendous rise in popularity of mobile services and Internet applications. Since IMS uses Session Initiation Protocol as the main protocol to control a signal, it inherits numerous known security vulnerabilities. One of the most severe issues is the Denial of Service attack. To address this problem, we introduce an anomaly-based detection system using the Tanimoto distance to identify deviations in the traffic. A modified moving average is applied to compute an adaptive threshold. To overcome a drawback of the adaptive threshold method, we present a momentum oscillation indicator to detect a gradually increasing attack. Generally, anomaly-based detection systems trigger many alarms and most of them are false positives that impact the quality of the detection. Therefore, we first present a false positive reduction method by using a trust model. A reliable trust value is calculated through the call activities and the human behavior of each user. The system performance is evaluated by using a comprehensive synthetic dataset containing various malicious traffic patterns. The experimental results show that this system accurately identified attacks and has the flexibility to deal with many types of attack patterns with a low false alarm. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.22(2014) No.2 (online) DOI http://dx.doi.org/10.2197/ipsjjip.22.118 ------------------------------ |
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収録物識別子タイプ | NCID | |||||||||||||
収録物識別子 | AN00116647 | |||||||||||||
書誌情報 |
情報処理学会論文誌 巻 55, 号 2, 発行日 2014-02-15 |
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ISSN | ||||||||||||||
収録物識別子タイプ | ISSN | |||||||||||||
収録物識別子 | 1882-7764 |