Email spam detection and filtering using machine learning

Phishing assaults, in which the perpetrator masquerades as a legitimate source in order to obtain confidential material, are now a serious threat due to the rapid growth of online consumers damaging one’s credibility, costing one’s money, or infecting one’s computer with spyware and perhaps other viruses. Due to their capacity to sift through large amounts of data in search of patterns that can be used to make predictions, intelligent approaches like ML & DL were finding growing usage in the realm of cybersecurity. In this study, we explore the efficacy of using such clever methods to identify phishing websites. We utilized two different data sets and picked the most highly linked attributes, which included both content-based and URL-lexical/domain-based characteristics. After that, many ML models were implemented, and their relative efficacy was assessed. The results demonstrated the significance of selecting features in raising the quality of the models. In addition, the findings attempted to determine the most useful factors that affect the model when it comes to recognizing phishing websites. When it came to classifying data, the Random Forest (RF) algorithm performed best across the board.