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• W50596167 abstract "Phishing emails are a real threat to internet communication and web economy. Criminals are trying to convince unsuspecting online users to reveal passwords, account numbers, social security numbers or other personal information. Filtering approaches using blacklists are not completely effective as about every minute a new phishing scam is created. We investigate the statistical filtering of phishing emails, where a classifier is trained on characteristic features of existing emails and subsequently is able to identify new phishing emails with different contents. We propose advanced email features generated by adaptively trained Dynamic Markov Chains and by novel latent Class-Topic Models. On a publicly available test corpus classifiers using these features are able to reduce the number of misclassified emails by two thirds compared to previous work. Using a recently proposed more expressive evaluation method we show that these results are statistically significant. In addition we successfully tested our approach on a non-public email corpus with a real-life composition." @default.
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• W50596167 date "2008-01-01" @default.
• W50596167 modified "2023-09-26" @default.
• W50596167 title "Improved Phishing Detection using Model-Based Features." @default.
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