The team aims to detect phishing links using machine learning and natural language processing techniques. They will mark phishing links as spam or harmful and prevent users from clicking on them. Their proposed solution is to create a system that can detect and warn users about phishing links. They will analyze URLs, domain information, page content, and other features to classify domains as phishing or legitimate using a trained machine learning model. Their goal is to increase awareness and provide security by preventing users from interacting with harmful sites.

1. Detection of Phising Links and
prevention from cyber attacks
Team : Cyber_Cops
● Likhitha Bommineni (leader)
● Divya Anand
● Nithiyashree
● Rachit Lamba
● Supriya Anand

2. Contents :
● Team Strength
● Problem Statement
● Supporting Data
● Timeline
● Proposed Solution
● Final Objective
● Approach & Implementation

3. Team Strength :
● Excellent Communication and Understanding.
● We belong to a diverse background and from different branches
of Engineering.
● We are beginners, but we are very interested and have a high
passion for Cyber security.
● We are organized and eager to learn in this field.

4. Problem Statement:
Phishing attacks are one of the most common security challenges that both
individuals and companies face in keeping their information secure. Whether
it's getting access to passwords, credit cards, or other sensitive information,
hackers are using email, social media, phone calls, and any form of
communication they can to steal valuable data. Businesses, of course, are a
particularly worthwhile target.
Contd..

5. The given data shows how
people are trapped and their
important information is
extracted.
Many users unwittingly click
phishing domains every day
and every hour. The attackers
are targeting both the users
and the companies.
We aim at preventing users
from performing on-click
events.

6. Contd..

7. Timeline &
Planning of
Phishing
Events

8. Proposed Solution
● Objectives:
○ Detection of Phishing Sites/Links using ML and NLP Processing
techniques
○ Marking it Spam/Harmful
○ Prevention of the on-click events by the users

9. Overview & Final Objective:
The main reason for the high amount of on-click events is the lack of
awareness among the users. But in order to defend security we have
to take precautions and come-up with solutions in order to prevent
users from confronting these harmful sites.
We aim at creating a system which can detect and mark all the
phishing links and warn the user about proceeding with them.

10. Implementation and Approach
● Detecting Phishing Domains is a classification problem, so it means
we need labeled data which has samples as phish domains and
legitimate domains in the training phase.
● By checking the URL structure we can get the clear understanding of
how attackers think when they create a phishing domain.
● URL is the first thing to analyze a website to decide whether it is a
phishing or not. As we mentioned before, URLs of phishing domains
have some distinctive points. Features which are related to these
points are obtained when the URL is processed.

11. Features Used for Phishing Domain
Detection
1. URL-Based Features
2. Domain-Based Features
3. Page-Based Features
4. Content-Based Features

12. Structure of URL
Uniform Resource Locator (URL) is created to address web pages. The figure below shows
relevant parts in the structure of a typical URL.

13. URL -Based Features
 Digit count in the URL
 Total length of URL
 Checking whether the URL is Typo-squatted or not. (google.com →
goggle.com)
 Checking whether it includes a legitimate brand name or not (apple-
icloud-login.com)
 Number of subdomains in URL
 Is Top Level Domain (TLD) one of the commonly used one?

14. Thank You