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ABSTRACT
Phishing websites have proven to be a major security concern. Several
cyberattacks risk the confidentiality, integrity, and availability of company and
consumer data, and phishing is the beginning point for many of them. Many
researchers have spent decades creating unique approaches to automatically
detect phishing websites. While cutting-edge solutions can deliver better
results, they need a lot of manual feature engineering and aren’t good at
identifying new phishing attacks. As a result, finding strategies that can
automatically detect phishing websites and quickly manage zero-day phishing
attempts is an open challenge in this field. The web page in the URL which
hosts that contains a wealth of data that can be used to determine the web
server’s maliciousness. Machine Learning is an effective method for detecting
phishing. It also eliminates the disadvantages of the previous method. We
conducted a thorough review of the literature and suggested a new method for
detecting phishing websites using features extraction and a machine learning
algorithm. The goal of this research is to use the dataset collected to train ML
models and deep neural nets to anticipate phishing websites.
Abstract:
In the digital age, the threat of phishing attacks poses significant risks to individuals and organizations alike. This postgraduate project, “Phishing Website Detection using Machine Learning Algorithms,” addresses the pressing need for a robust system to identify and mitigate the proliferation of phishing websites. By leveraging Python and web technologies, coupled with machine learning algorithms, this project aims to provide a proactive defense against cyber threats.
Existing System:
Current methods of detecting phishing websites often rely on rule-based heuristics and databases of known malicious URLs. These approaches are limited in their ability to adapt to evolving phishing techniques and struggle to identify previously unseen threats.
Proposed System:
The proposed system introduces a dynamic and adaptive approach to phishing detection by incorporating machine learning algorithms. By training on a diverse dataset of features extracted from both legitimate and phishing websites, the system aims to generalize its learning to identify new, previously unknown phishing threats.
Problem Statement:
Phishing attacks continue to exploit vulnerabilities in traditional detection methods, leading to increased incidents of data breaches and financial losses. The lack of a proactive and adaptive system hampers the ability to respond effectively to emerging phishing threats.
Motivation:
This project is motivated by the imperative to develop an advanced and autonomous phishing detection system. By integrating machine learning algorithms, the project seeks to enhance the accuracy and efficiency of phishing website identification, ultimately strengthening cybersecurity measures for individuals and organizations.
Modules Explanation:
1. Data Collection and Preprocessing:
– Gathering a diverse dataset of features from both legitimate and phishing websites.
– Preprocessing data to extract relevant features and prepare it for machine learning.
2. Machine Learning Model Training:
– Utilizing machine learning algorithms (e.g., Random Forest, Decision Trees) to train the model on the prepared dataset.
3. Real-time Phishing Detection:
– Implementing a real-time detection mechanism that classifies websites as legitimate or phishing based on the trained model.
System Requirements:
Frontend:
– HTML5, CSS3, JavaScript for the web interface.
– React.js for dynamic and responsive user interactions.
Backend:
– Python for machine learning model implementation.
– Flask or Django for web server development.
Database:
– MongoDB or MySQL for storing training data and model parameters.
Algorithms:
Machine Learning Algorithms:
– Random Forest, Decision Trees for classification.
Hardware and Software Requirements:
Hardware:
– Standard computing devices with internet connectivity.
Software:
– Modern web browsers (Google Chrome, Mozilla Firefox).
– Python environment with necessary libraries (scikit-learn, pandas).
– Flask or Django for backend development.
Architecture:
The system follows a client-server architecture where the React.js-based frontend communicates with the Python backend for phishing website detection. The trained machine learning model is integrated into the backend for real-time analysis.
Technologies Used:
Frontend:
– React.js, HTML5, CSS3, JavaScript.
Backend:
– Python, Flask or Django.
Database:
– MongoDB or MySQL.
Machine Learning:-
scikit-learn, pandas.
Web User Interface:
The web interface is designed to be intuitive, providing users with real-time feedback on the legitimacy of websites. Alerts and visual cues guide users in making informed decisions about the safety of webpages, thereby enhancing cybersecurity awareness.
This project endeavors to bolster cybersecurity efforts by introducing an intelligent and adaptive approach to phishing website detection. By integrating machine learning algorithms into the detection process, the system aims to stay ahead of evolving cyber threats, providing a more resilient defense against phishing attacks.
UML DIAGRAMS
Collaboration Diagram
Architecture diagram
class diagram
sequence diagram
use case diagram
activity diagram
component diagram
Deployment Diagram
Flow chart Diagram
