| Presentation | 2018-11-21 Spoofed Website Detection using Machine Learning Naoki Kurihara, Hidenori Tsuji, Masaki Hashimoto, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In recent years, the damage by fake site has been rapidly increasing. Because fake sites are ceremonious as if they are genuine, it is difficult to simply detect a fake site, and in the present situation, because humans are conducting surveys one by one for many sites that exist , It takes a considerable amount of time to detect fake sites. In this research, in order to solve this problem, by developing a fake site detection method using machine learning, it is possible to automate the detection process reliant on human hands to some extent and shorten the time taken for fake site detection aim. In this paper, for this purpose, we present the current status and problems of fake site detection and report the result of two experiments for developing new method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | HTML / Machine Learning / Support Vector Machine / Multilayer Perceptron / Spoofed Website / Natural language processing / doc2vec |
| Paper # | ICSS2018-56 |
| Date of Issue | 2018-11-14 (ICSS) |
| Conference Information | |
| Committee | ICSS |
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| Conference Date | 2018/11/21(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Yoshiaki Shiraishi(Kobe Univ.) |
| Vice Chair | Hiroki Takakura(NII) / Katsunari Yoshioka(Yokohama National Univ.) |
| Secretary | Hiroki Takakura(NTT) / Katsunari Yoshioka(NICT) |
| Assistant | Akira Yamada(KDDI labs.) / Keisuke Kito(Mitsubishi Electric) |
| Paper Information | |
| Registration To | Technical Committee on Information and Communication System Security |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Spoofed Website Detection using Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | HTML |
| Keyword(2) | Machine Learning |
| Keyword(3) | Support Vector Machine |
| Keyword(4) | Multilayer Perceptron |
| Keyword(5) | Spoofed Website |
| Keyword(6) | Natural language processing |
| Keyword(7) | doc2vec |
| 1st Author's Name | Naoki Kurihara |
| 1st Author's Affiliation | Institute of Information Security(Institute of Information Security) |
| 2nd Author's Name | Hidenori Tsuji |
| 2nd Author's Affiliation | Institute of Information Security(Institute of Information Security) |
| 3rd Author's Name | Masaki Hashimoto |
| 3rd Author's Affiliation | Institute of Information Security(Institute of Information Security) |
| Date | 2018-11-21 |
| Paper # | ICSS2018-56 |
| Volume (vol) | vol.118 |
| Number (no) | ICSS-315 |
| Page | pp.pp.19-24(ICSS), |
| #Pages | 6 |
| Date of Issue | 2018-11-14 (ICSS) |