Abstract

Spam analysis and classification of dynamic messages is an essential task in order to combat the ever-increasing volume of unsolicited and malicious emails. One effective approach is to employ a vectorizing technique along with a multi-model machine learning algorithm. This approach involves representing email messages as high-dimensional vectors, capturing various features such as word frequencies, presence of specific keywords, and structural characteristics. By transforming the text into numerical representations, the machine learning algorithm can then learn patterns and make predictions based on these representations. The use of a multimodel algorithm allows for the integration of different classification models, each with its own strengths and weaknesses, to enhance the overall performance. This approach can achieve high accuracy by leveraging diverse learning methods and combining their predictions. Furthermore, the approach is dynamic in nature, meaning that it can adapt to new forms of spam and evolving attack strategies. The key challenge lies in selecting appropriate features and tuning the parameters of the algorithm to ensure optimal performance. The results of this study can contribute to the development of more effective and efficient spam detection systems, helping users to filter out unwanted and potentially harmful messages.