2022/06/02 by Forough Farazmanesh, Farazmanesh, Forough, Fateme Foroutan +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Authorship Attribution and Profiling #Computers and Society (cs.CY) #Cryptography and Security (cs.CR) #Cybercrime and Law Enforcement Studies #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #Spam and Phishing Detection #cs.AI #cs.CR #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.2206.03581
openalex publication_date 2022/06/02 · arxiv created 2022/06/09 · arxiv updated 2022/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.