Phishing short URL detection based on link jumping on social networks
Bailin Xie, Qi Li, Na Wei · ITM Web of Conferences · 2022
Nowadays, a large number of people frequently use social networks. Social networks have become important platforms for people to publish and obtain information. However, social networks have also become the main venue for hackers to initiate online fraud. Phishing is a common way used by hackers to launch online fraud on social networks. This paper proposes a method for detecting phishing short URL based on the link jumping. The method uses a hierarchical hidden Markov model with two-layer structure to describe the link jumping process after user clicking on short URL, so as to identify phishing short URL on social networks. The proposed method includes a training phase and an identification phase. In the identification phase, the average log-likelihood probability of the observation sequence is calculated. An experiment based on real datasets of Weibo is conducted to evaluate this method. The experiment results validate the effectiveness of this method.