Abnormal Traffic Detection Method Based on Weighted Bayesian
Long Zhang, Hui Wang · 2024
Email has a fast dissemination speed and low cost, and has been widely used in people's daily work and study. However, the proliferation of spam emails, such as a large number of advertising emails flooding mailboxes, has had a significant impact on people's normal use of email, and even phishing emails and virus containing emails are mixed in, posing a serious threat to the security of email users. Given the harm of spam emails, it has become increasingly urgent to study how to better suppress their spread. This paper proposes a weighted Bayesian algorithm for classifying and detecting spam emails. Through learning from email samples, spam filtering is performed, and the ratio of spam emails to normal emails is based on the percentage of spam emails in user emails in the "China Anti spam Status Survey Report". The experimental data reflects the effectiveness of this method in intercepting spam emails.