Compromised account detection using authorship verification: a novel approach

TL;DR

This paper introduces a new authorship verification method for early detection of compromised Twitter accounts using only the latest post features, achieving high accuracy in real-world tests.

Contribution

It presents a novel approach that enables early detection of compromised accounts by analyzing only recent user posts, improving response time and accuracy.

Findings

Achieved 89% detection accuracy on real-world Twitter data.

Effective for early detection with minimal data from last post.

Suitable for real-time monitoring of social media accounts.

Abstract

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%.