/IBM-Project-16931-1659625158

Web Phishing Detection

Primary LanguageJupyter NotebookMIT LicenseMIT

web1

Web Phishing Detection 👋

IBM-Project-16931-1659625158

Batch Name : B4-4M6E

Team ID : PNT2022TMID03926

Team Members : 😃

  • Lokesh P
  • Prithiv Raj S
  • Thirugnanam M
  • Brihadeeswaran N

Project Objectives : Bomb

By the end of this project :

  • We'll be able to understand the problem to classify if it is a regression or a classification kind of problem.
  • We will be able to know how to pre-process/clean the data using different data pre-processing techniques.
  • Applying different algorithms according to the dataset
  • We will be able to know how to find the accuracy of the model.
  • We will be able to build web applications using the Flask framework.

Introduction : 🎊

There are a number of users who purchase products online and make payments through e-banking. There are e-banking websites that ask users to provide sensitive data such as username, password & credit card details, etc., often for malicious reasons. This type of e-banking website is known as a phishing website. Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet.

Common threats of web phishing : Exploding Head

  1. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity.
  1. It will lead to information disclosure and property damage.
  1. Large organizations may get trapped in different kinds of scams.
  1. This Guided Project mainly focuses on applying a machine-learning algorithm to detect Phishing websites.

In order to detect and predict e-banking phishing websites, we proposed an intelligent, flexible and effective system that is based on using classification algorithms. We implemented classification algorithms and techniques to extract the phishing datasets criteria to classify their legitimacy. The e-banking phishing website can be detected based on some important characteristics like URL and domain identity, and security and encryption criteria in the final phishing detection rate. Once a user makes a transaction online when he makes payment through an e-banking website our system will use a data mining algorithm to detect whether the e-banking website is a phishing website or not.

Technical Architecture : Brick

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DEMO VIDEO

Project.Name.mp4