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講演抄録/キーワード
講演名 2017-11-15 13:50
Machine Learning Approach for Phishing Detection in SDN Networking
Yu-Hung ChenJiun-Yu YangPo-Chun HouJiann-Liang ChenNational Taiwan University of Science & Technology
技報オンラインサービス実施中
抄録 (和) People have become increasingly dependent on information technology since the emergence of the Internet. Therefore, many hackers engage in financial crimes and computer attacks through the Internet. The existing attack modes include a combination of Trojans, Botnet, social engineering, and email phishing technology. In recent years, the rise of numerous phishing attacks has led to great money loss on the part of people deceived by phishing attacks. In general, phishing websites have a short lifetime, and the attack technique is relatively more complex, thus drawing information security concern. In this study, the software-defined infrastructure was adopted to deploy a platform where automated phishing website risk analysis, assessment, and strategic executions and operations were conducted. During the experimentation period, 25,000 phishing websites were obtained using Common Crawl, and 25,000 illegal websites were obtained from Phishing Tank. Machine learning was conjunctively used in this study to complete the research and development of the phishing website learning model creation, risk analysis, hazard assessment, and other mechanisms. Software-defined networking was subsequently used to carry out strategic execution targeting hazardous messages in order to ensure a high degree of overall network environment security. The experimental results show that the training model in this study reaches an accuracy of 96.97%. 
(英) People have become increasingly dependent on information technology since the emergence of the Internet. Therefore, many hackers engage in financial crimes and computer attacks through the Internet. The existing attack modes include a combination of Trojans, Botnet, social engineering, and email phishing technology. In recent years, the rise of numerous phishing attacks has led to great money loss on the part of people deceived by phishing attacks. In general, phishing websites have a short lifetime, and the attack technique is relatively more complex, thus drawing information security concern. In this study, the software-defined infrastructure was adopted to deploy a platform where automated phishing website risk analysis, assessment, and strategic executions and operations were conducted. During the experimentation period, 25,000 phishing websites were obtained using Common Crawl, and 25,000 illegal websites were obtained from Phishing Tank. Machine learning was conjunctively used in this study to complete the research and development of the phishing website learning model creation, risk analysis, hazard assessment, and other mechanisms. Software-defined networking was subsequently used to carry out strategic execution targeting hazardous messages in order to ensure a high degree of overall network environment security. The experimental results show that the training model in this study reaches an accuracy of 96.97%.
キーワード (和) Phishing detection / Software-Defined Networking / Extreme Learning Machine / Ensemble Learning / Feature engineering / / /  
(英) Phishing detection / Software-Defined Networking / Extreme Learning Machine / Ensemble Learning / Feature engineering / / /  
文献情報 信学技報, vol. 117, no. 299, IA2017-30, pp. 1-6, 2017年11月.
資料番号 IA2017-30 
発行日 2017-11-08 (IA) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380

研究会情報
研究会 IA  
開催期間 2017-11-15 - 2017-11-16 
開催地(和) KMITL, タイ・バンコク 
開催地(英) KMITL, Bangkok, Thailand 
テーマ(和) IA2017 - Workshop on Internet Architecture and Applications 2017 
テーマ(英) IA2017 - Workshop on Internet Architecture and Applications 2017 
講演論文情報の詳細
申込み研究会 IA 
会議コード 2017-11-IA 
本文の言語 英語 
タイトル(和)  
サブタイトル(和)  
タイトル(英) Machine Learning Approach for Phishing Detection in SDN Networking 
サブタイトル(英)  
キーワード(1)(和/英) Phishing detection / Phishing detection  
キーワード(2)(和/英) Software-Defined Networking / Software-Defined Networking  
キーワード(3)(和/英) Extreme Learning Machine / Extreme Learning Machine  
キーワード(4)(和/英) Ensemble Learning / Ensemble Learning  
キーワード(5)(和/英) Feature engineering / Feature engineering  
キーワード(6)(和/英) /  
キーワード(7)(和/英) /  
キーワード(8)(和/英) /  
第1著者 氏名(和/英/ヨミ) Yu-Hung Chen / Yu-Hung Chen /
第1著者 所属(和/英) National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
第2著者 氏名(和/英/ヨミ) Jiun-Yu Yang / Jiun-Yu Yang /
第2著者 所属(和/英) National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
第3著者 氏名(和/英/ヨミ) Po-Chun Hou / Po-Chun Hou /
第3著者 所属(和/英) National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
第4著者 氏名(和/英/ヨミ) Jiann-Liang Chen / Jiann-Liang Chen /
第4著者 所属(和/英) National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
National Taiwan University of Science & Technology (略称: National Taiwan University of Science & Technology)
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講演者
発表日時 2017-11-15 13:50:00 
発表時間 25 
申込先研究会 IA 
資料番号 IEICE-IA2017-30 
巻番号(vol) IEICE-117 
号番号(no) no.299 
ページ範囲 pp.1-6 
ページ数 IEICE-6 
発行日 IEICE-IA-2017-11-08 


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