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Title of Journal: Multimed Tools Appl

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Abbravation: Multimedia Tools and Applications

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Springer US

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1573-7721

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Authorship verification applied to detection of co

Authors: Sylvio Barbon Rodrigo Augusto Igawa Bruno Bogaz Zarpelão
Publish Date: 2016/09/05
Volume: 76, Issue: 3, Pages: 3213-3233
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Abstract

Compromising legitimate accounts has been the most used strategy to spread malicious content on OSN Online Social Network To address this problem we propose a pure text mining approach to check if an account has been compromised based on its posts content In the first step the proposed approach extracts the writing style from the user account The second step comprehends the kNearest Neighbors algorithm kNN to evaluate the post content and identify the user Finally Baseline Updating third step consists of a continuous updating of the user baseline to support the current trends and seasonality issues of user’s posts Experiments were carried out using a dataset from Twitter composed by tweets of 1000 users All the three steps were individually evaluated and the results show that the developed method is stable and can detect the compromised accounts An important observation is the Baseline Updating contribution which leads to an enhancement of accuracy superior of 60 Regarding average accuracy the developed method achieved results over 93

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