Email Spam Classification Using Supervised Learning in Different Languages
Aryan Rawat, Shiddhant Behera, V. Rajaram · 2022
Communication has grown exponentially in recent times. Current generation envisions email as one of the fastest modes of communication for shorter and longer distances. The constant development of spam email attacks and the harmful go-aheads inherent in these attacks spread across a big variety of business, personal and social activities have created a requirement for an intelligent automated spam detection system. Attempts to facilitate malware, steal sensitive data, steal identity and cause financial and reputational damage are on the rise, putting customers' privacy in danger. Current solutions are rather imperfect considering the multifaceted functional scope of email, but they are considered. The proposed work examined the application of supervised learning to clustering spam and ham emails. With machine learning technology, tools suitable for various languages. Spam detection for Chinese, Russian, Arabic, Italian, French, German, and English emails is developed. Experimental results were compared in terms of different machine learning classification models to propose an optimal solution