Email Security Threat Detection

Abstract

Email has turned into one of the major avenues through which hackers initiate cyber-attacks, mainly phishing, a situation in which attackers deceive users into giving up confidential information. It is absolutely necessary to detect such harmful emails as soon as possible to alleviate the cases of data breaches and financial losses. This is a study of a machinelearning-based method to recognize phishing emails by using content as well as attachments analysis. The system proposed derives the characteristics related to language and structure of the email from the use of Natural Language Processing (NLP) techniques like tokenization, stop-word removal, and frequency analysis. Random Forest classifier is trained on a labeled dataset of legitimate and phishing emails so as to be able to assess the threat level of new messages. The model scores are quite good in terms of accuracy, precision, and recall, thus ensuring that the false alarms are kept at a minimum.