Compromising legitimate accounts is a way of disseminating malicious content to a large user base in Online Social Networks (OSNs). Since the accounts cause lots of damages to the user and consequently to other users on OSNs, early detection is very important. This paper proposes a novel approach based on authorship verification to identify compromised twitter accounts. As the approach only uses the features extracted from the last user's post, it helps to early detection to control the damage. As a result, the malicious message without a user profile can be detected with satisfying accuracy. Experiments were constructed using a real-world dataset of compromised accounts on Twitter. The result showed that the model is suitable for detection due to achieving an accuracy of 89%.
翻译:将合法账户混为一谈是向在线社会网络(OSNs)的大型用户库传播恶意内容的一种方式。由于这些账户对用户以及因此对OSNs上的其他用户造成大量损害,早期发现非常重要。本文件提出基于作者核查的新颖方法,以识别已失密的Twitter账户。由于该方法只使用从最后一个用户的邮政中提取的特征,因此有助于早期发现以控制损坏。因此,可以以令人满意的准确性检测出没有用户概况的恶意信息。实验是利用在Twitter上的失密账户真实世界数据集进行的。结果显示,该模型由于精确度达到89%,因此适合检测。