Compromised account detection using authorship verification: a novel approach
This addresses the issue of malicious content spread via compromised accounts in Online Social Networks, offering an early detection method to reduce damage, though it appears incremental in nature.
The paper tackled the problem of detecting compromised Twitter accounts by using authorship verification on users' last posts, achieving an accuracy of 89% in experiments with a real-world dataset.
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%.