Social media networks usage is spreading but accompanied by a new shape of the social engineering attacks in which users’ accounts are compromised by attackers to spread malicious messages for different purposes. To overcome these attacks, authorship verification, a classification problem for classifying a text, whether it belongs to a user or not, is needed. Moreover, the verification must be accurate and fast. Herein, an authorship verification model proposed. The model uses XGBoost, as a preprocessor, to discover functional features of the text message, which ranked using MCDM methods to build a classification model. Twitter messages are used to test the model; however, any social media’s data might be used. The suggested model was evaluated against a crawled dataset from Twitter composed of 16124 tweets with 280 characters. The proposed method achieved F-score over 0.94.

社交媒体网络的使用正在扩散，但伴随着一种新形式的社交工程攻击，其中攻击者破坏了用户的帐户，以出于不同目的传播恶意消息。为了克服这些攻击，需要作者身份验证，即用于对文本进行分类的分类问题，无论文本是否属于用户。此外，验证必须准确，快速。在此，提出了作者身份验证模型。该模型使用XGBoost作为预处理器来发现文本消息的功能，然后使用MCDM方法对其进行排名以构建分类模型。Twitter消息用于测试模型；但是，可以使用任何社交媒体的数据。根据Twitter的爬网数据集（包含16124条280个字符的推文）对建议的模型进行了评估。