Intelligent Phishing Detection and Spam Classification System Using Rule-Based Text Analysis

Abstract

With the rapid growth of digital communication, email and web-based interactions have
become essential components of daily life. However, this growth has also led to a
significant rise in cyber threats, particularly phishing attacks, which aim to deceive users
into revealing sensitive information such as passwords, banking details, and personal
credentials. This project presents an intelligent phishing detection and spam classification
system developed using a rule-based text analysis approach within a Django web
framework.The system is designed to identify and categorize phishing attacks and spam
messages based on predefined keyword patterns and heuristic rules. It consists of
multiple modules, including user authentication, phishing detection during login, email
composition and classification, malicious URL checking, and user feedback collection.
When a user attempts to log in, the system analyzes the entered email string using regular
expressions and keyword matching techniques to detect potential phishing indicators such
as suspicious domains, numeric patterns, and misleading structures. Based on this
analysis, the system classifies the input into categories like spear phishing, whaling
phishing, pharming attacks, or search engine phishing.
In addition to login analysis, the system includes an email classification module that
processes message content and categorizes it into different domains such as financial,
social networking, cloud storage, and others. It also determines whether an email should
be marked as spam or delivered to the inbox based on the presence of malicious or
suspicious keywords. This classification helps users identify harmful content before
interacting with it.Another key feature is the phishing thread detection module, which
analyzes user-provided text or URLs to identify hidden malicious patterns related to
sensitive information such as API keys, bank IDs, and private identifiers