YouTube, a prominent online platform hosting millions of users globally, is experiencing a concerning surge in unusual activities. This paper introduces a scoring method that integrates engagement metrics and commenter behavior features to evaluate anomalies in YouTube channels. By examining engagement indicators such as comments, views, and subscriber numbers and with analyzing frequent commenter groups, the study employs various techniques including Cosine Similarity and Principal Component Analysis, along with average and maximum aggregation methods. Our dataset includes 71 channels, 642,952 videos, 12,425,587 commenters, 123,882,200 comments, 83,396,188,807 views, and 139,985,870 subscribers. Focusing on channels related to the US Air Force, news, and the Indo-Pacific region, the research offers insights into anomalous activities on YouTube. Our objective in this study is to establish a secure online space while maintaining the credibility of YouTube.