A Kind of Identity Authentication Method Based on Browsing Behaviors

Due to the continued growth threat in Phishing, a kind of stable identity authentication method is highly needed based on individual characteristics just like browsing behaviors. Most of the existing researches focused on browsing behavior patterns of group users are used in personal recommendation, website structure optimization or web prediction. In order to ensure the validity of user identity and the security of e-commerce, we construct personalized user browsing behavior model based on ARM (Association Rule Mining) from Web usage log. We compare real-time browsing behaviors with history model to identify a user's real identity in Web pages accessed. According to the results of the experiments, for the illegal users, this method can attain 91.3% detection rate with below 10% false alarm rate. Thus, it can achieve high real-time and recognition efficiency.