Spam Mail Detection Using Machine Learning and Deep Learning Algorithm

Email spam remains a significant threat to the modern Internet, posing risks such as data breaches, cyber-attacks, and loss of personal information. In response, anti-spam filters have been developed to mitigate this issue. These filters face the challenge of accurately classifying emails in personalized mailboxes as either spam or legitimate (ham). Researchers have explored various stylistic features in text messages to aid in this classification. The identification of spam emails frequently depends on the use of well-known words, idioms, phrases, and acronyms. The primary goal of this study is to compare different classification techniques using datasets from prior research. These techniques will be evaluated based on their accuracy, recall, and precision. The comparison encompasses both traditional machine learning methods and newer approaches. Thus, it is essential to propose effective mechanisms for detecting or identifying spam emails, as this can significantly improve system efficiency in terms of time and memory usage.