DETECTING PHISHING WEBSITES USING MACHINE LEARNING
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DETECTING PHISHING WEBSITES USING MACHINE LEARNING
Chapter One: Introduction
1.1 Background of the Study
It is critical for all computer and internet users to keep information safe and secure, as well as to minimise the possibility of fraud that may occur while accessing various websites.
Phishing is a problem that has been identified since the dawn of the internet age, and it is one of the most difficult to prevent and control.
Phishing is a type of cybercrime in which someone impersonating a legitimate institution contacts a target or targets via email, phone, or text message in order to trick them into providing sensitive data such as personally identifiable information, banking and credit card information, and passwords.
The information is then utilised to gain access to crucial accounts, potentially leading to identity theft and financial loss.
The first phishing case was filed in 2004 against a California teenager who developed a replica of the “America Online” website. He was able to obtain sensitive information from individuals and access credit card information in order to withdraw money from their accounts via this fraudulent website.
In addition to email and internet phishing, fraudsters are continually devising new phishing strategies such as ‘vishing’ (voice phishing),’smishing’ (SMS phishing), and others. The study’s goal is to focus on the various methods of phishing and potential solutions in the form of machine learning software.
1.2 Statement of the Problem
Phishing has previously been described as a fraudulent attempt to gain sensitive data in order to commit a crime. Phishing sites can be difficult to identify by average users unless they know the specific URL, which can be time-consuming.
In this situation, we would create an artificially intelligent machine learning system to perform this detection with near-perfect accuracy.
1.3 Motivation for the Study
This study was prompted by the loss of millions of dollars owing to fraudsters operating phoney versions of data gathering websites, as well as the need for a safer internet experience as we go forward in the internet and communication era.
1.4 Aims and Objectives of the Study
The goals and objectives of this system include:
Create a phishing detection system.
Developing a reporting facility allowing other platform users to report bogus websites in order to expand the knowledge base.
Examining past work on the proposed topic and seeking methods to improve it.
Optimising the system.
Implementing security standards in the system.
Creating a system that can also make suggestions to visitor users
1.5 Methodology
This idea and project were implemented using the server-side programming language PHP and the MySQL database querying language.
These languages were chosen based on their server-side compatibility, high level of security, and overall web compatibility. Front-end technologies employed include HTML, CSS, and JavaScript. This will also be served as a web application through an online domain.
1.6 SCOPE OF THE STUDY
The system’s breadth broadens over time. The system collects user feedback and incorporates it into the knowledge base. It also employs specific algorithms to identify phoney websites, particularly those that are frequently phished.
1.7 Significance of the Study
The study is significant because it can assist to lower the number of people who fall for phoney websites and disclose their personal information to them. It is quite useful for folks who do not have enough time to precisely identify exact URLs in order to validate the addresses.
1.8 Operational Definition of Terms
PHISHING: Phishing is an internet scam in which thieves mimic reputable organisations via email, text message, advertisement, or other means in order to get sensitive information.
CYBER CRIME: Cybercrime is defined as any criminal conduct that targets or uses a computer, a computer network, or a networked device. Most, but not all, cybercrime is done by cybercriminals or hackers looking to make money. Individuals or organisations commit cybercrimes.
URL: A URL, which stands for universal resource locator, contains the protocol (e.g., HTTP, FTP), the domain name (or IP address), and extra path information. A URL can go to a Web page, an image, or any other file that supports the HTTP protocol.
1.9 Organisation of the Project
The project is organised in such a way that Chapter One provides a quick introduction in the form of the Background of the Study, which provides a broad review of the notion of phishing and the combative tactics for it.
Chapter Two takes a thorough look at the various implementations of anti-phishing technologies. The third chapter looks at the system architecture, design, and analysis.
Chapter Four describes the project’s implementation utilising the relevant programming languages and development models. It also displays the results of the tests that were conducted. Chapter Five concludes the project work with a summary, findings, and recommendations for future projects.
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