Evaluating payload features for malware infection detection

• Item type: Journal(1)
• 公開日: 2014-02-15
• タイトル: Evaluating payload features for malware infection detection
• タイトル / 言語: en
• タイトル: Evaluating payload features for malware infection detection
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文(推薦論文)] malware, infection detection, traffic, payload
• 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: The University of Electro-Communications
• 著者所属: The University of Electro-Communications
• 著者所属: The University of Electro-Communications
• 著者所属: NTT Communications Corporation
• 著者所属: The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者所属(英): en
• 著者所属(英): NTT Communications Corporation
• 著者所属(英): en
• 著者所属(英): The University of Electro-Communications
• 著者名: Yusuke, Otsuki; Masatsugu, Ichino; Soichi, Kimura; Mitsuhiro, Hatada; Hiroshi, Yoshiura
• 著者名(英): Yusuke, Otsuki; Masatsugu, Ichino; Soichi, Kimura; Mitsuhiro, Hatada; Hiroshi, Yoshiura
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Analysis of malware-infected traffic data revealed the payload features that are the most effective for detecting infection. The traffic data was attack traffic using the D3M2012 dataset and CCC DATAsets 2009, 2010, and 2011. Traffic flowing on an intranet at two different sites was used as normal traffic data. Since the type of malware (worm, Internet connection confirmation, etc.) affects the type of traffic generated, the malware was divided into three types―worm, Trojan horse, and file-infected virus―and the most effective features were identified for each type.; ------------------------------; 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.22.376; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Analysis of malware-infected traffic data revealed the payload features that are the most effective for detecting infection. The traffic data was attack traffic using the D3M2012 dataset and CCC DATAsets 2009, 2010, and 2011. Traffic flowing on an intranet at two different sites was used as normal traffic data. Since the type of malware (worm, Internet connection confirmation, etc.) affects the type of traffic generated, the malware was divided into three types―worm, Trojan horse, and file-infected virus―and the most effective features were identified for each type.; ------------------------------; 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.22.376; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 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
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764