Enhancing Email Securly Automated Phishing Detection

Inspiration

Phishing attacks have long been a method used by cyber criminals, with the first known case occurring in the early 1990s on AOL chatrooms (Chaudhry and Rittenhouse, 2016). Attackers, posing as AOL employees, tricked users into revealing login credentials to access their credit card information. With advancements in email communication and the widespread availability of active email addresses, phishing has evolved from targeted deception to large-scale mass spamming. Phishing emails are a serious cybersecurity threat, commonly employed to steal organizational and personal information. Rule-based systems commonly do not identify these attacks because they are dynamic. This project seeks to create a system based on machine learning to identify phishing emails through both the content of the email and sender metadata. Through the use of natural language processing (NLP)and classification algorithms, the system identifies fake messages and improves email security.

What it does

The system has an Email Input Module which can fetch the mail when it is received in the email id, Flask Backend API, Trained ML Model (naïve Bayes /SVM), Classification Engine (Vectorizer and Prediction), and a Decision Output module. It takes meaningful features from the email body and metadata and classifies the email using machine learning algorithms like SVM or Random Forest. Emails are first extracted and preprocessed for phishing detection. The vectorizer converts the text content into a numerical format because text data (like email content) is unstructured and cannot be directly processed by ML algorithms. The trained ML model then analyzes these features to predict whether the email is phishing or legitimate, and the result is updated accordingly

How we built it

Phishing attacks demand an intelligent system which can learn from constantly changing threats.The classical filters fall short. This project solves the issue based on a machine learning algorithm trained to learn patterns in email content and sender metadata, providing a preemptive response to email security with better accuracy and adaptability. In large Corporations or enterprises like Microsoft, Google have High-value credentials and complex internal systems and also in the places like Financial Institutions, Universities & Educational Institutions, Healthcare Systems, E-commerce, Media & Telecommunication Companies where they are dealing with massive number of mails daily and in such mails, there is also some phishing mails to threat the data. By the phishing mail detecting software, the scam mails can be detected and the data can be saved from the threat.

Challenges we ran into

The developed system is a machine learning-based application designed to detect phishing emails by analysing both the content of the email and sender metadata. It uses natural language processing techniques to extract meaningful features and a trained classification model to accurately identify phishing attempts. This application can be integrated into email clients, organizational email gateways, or cloud-based security platforms to provide real-time protection against cyber threats. By automating phishing detection, the system enhances email security, reduces human error, and minimizes the risk of data breaches and financial loss.

Built With

• classification
• engine
• flask-backend-api
• trained-ml-model-(naive-bayes-/svm)
• vectorizer