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| Content Provider | ACM Digital Library |
|---|---|
| Author | Han, Weili Le, Yueran Cao, Ye |
| Abstract | In phishing and pharming, users could be easily tricked into submitting their username/passwords into fraudulent web sites whose appearances look similar as the genuine ones. The traditional blacklist approach for anti-phishing is partially effective due to its partial list of global phishing sites. In this paper, we present a novel anti-phishing approach named Automated Individual White-List (AIWL). AIWL automatically tries to maintain a white-list of user's all familiar Login User Interfaces (LUIs) of web sites. Once a user tries to submit his/her confidential information to an LUI that is not in the white-list, AIWL will alert the user to the possible attack. Next, AIWL can efficiently defend against pharming attacks, because AIWL will alert the user when the legitimate IP is maliciously changed; the legitimate IP addresses, as one of the contents of LUI, are recorded in the white-list and our experiment shows that popular web sites' IP addresses are basically stable. Furthermore, we use Naïve Bayesian classifier to automatically maintain the white-list in AIWL. Finally, we conclude through experiments that AIWL is an efficient automated tool specializing in detecting phishing and pharming. |
| Starting Page | 51 |
| Ending Page | 60 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781605582948 |
| DOI | 10.1145/1456424.1456434 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-10-31 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Anti-pharming Login user interface Anti-phishing Individual white-list Na?ve bayesian classifier |
| Content Type | Text |
| Resource Type | Article |
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