Abstract:

We suggest a novel Collaborative Reputation-based Vector Space Model (CRVSM) in this paper for the identification of spam email. across order to detect spam emails across a wide area, CRVSM employs a vector space model to describe the feature vectors in multidimensional vector space. To speed up email spam detection, we group the emails into five groups. With a maximum and lowest threshold range, we compute the maximum similarity measure to lower the amount of false positives and false negatives. In addition, we employ a reputation evaluation tool that assesses the reporter's level of credibility when verifying the email as spam or not. In terms of email spam detection, the CRVSM technique has good efficiency and good results. In terms of email spam detection, the CRVSM technique has good efficiency and good results. Utilising measures like false positive rate, false negative rate, detection accuracy, and detection time, the performance of the CRVSM model has been assessed. The performance results unmistakably demonstrate that CRVSM surpasses the currently used detection algorithms and effectively classifies emails that arrive as spam or non-spam with lower FPR and FNR values

Description:

Feature, Cluster, Collaborative, Vector, Spam Email, Similarity