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- ZHANG Wei
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
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- REN Huan
- University of Science and Technology of China
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- JIANG Qingshan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
抄録
Phishing attacks target financial returns by luring Internet users to exposure their sensitive information. Phishing originates from e-mail fraud, and recently it is also spread by social networks and short message service (SMS), which makes phishing become more widespread. Phishing attacks have drawn great attention due to their high volume and causing heavy losses, and many methods have been developed to fight against them. However, most of researches suffered low detection accuracy or high false positive (FP) rate, and phishing attacks are facing the Internet users continuously. In this paper, we are concerned about feature engineering for improving the classification performance on phishing web pages detection. We propose a novel anti-phishing framework that employs feature engineering including feature selection and feature extraction. First, we perform feature selection based on genetic algorithm (GA) to divide features into critical features and non-critical features. Then, the non-critical features are projected to a new feature by implementing feature extraction based on a two-stage projection pursuit (PP) algorithm. Finally, we take the critical features and the new feature as input data to construct the detection model. Our anti-phishing framework does not simply eliminate the non-critical features, but considers utilizing their projection in the process of classification, which is different from literatures. Experimental results show that the proposed framework is effective in detecting phishing web pages.
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E99.D (4), 1062-1070, 2016
一般社団法人 電子情報通信学会
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キーワード
詳細情報 詳細情報について
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- CRID
- 1390282679354401280
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- NII論文ID
- 130005141401
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可