This project aims to detect phishing URLs using machine learning techniques. Phishing URLs are malicious links that attempt to trick users into revealing sensitive information such as passwords, credit card numbers, or personal information. By employing machine learning algorithms, this project helps identify and classify such URLs, thereby enhancing online security.
Phishing attacks have become increasingly sophisticated, making it challenging for users to distinguish between legitimate and malicious URLs. This project leverages machine learning models to analyze the characteristics of URLs and classify them as either phishing or legitimate. By utilizing features such as URL length, domain age, and presence of suspicious keywords, the model can accurately identify potential threats.
The dataset used in this project consists of a collection of URLs labeled as phishing or legitimate. It is essential to have a diverse and representative dataset to train the machine learning model effectively. The dataset used here can be found at [insert dataset link].
To run this project locally, you will need the following dependencies:
- Python 3.x
- NumPy
- Pandas
- Scikit-learn
- Jupyter Notebook (optional, for running the notebooks)
You can install these dependencies using pip:
pip install numpy pandas scikit-learn jupyterTo use this project, follow these steps:
- Clone the repository to your local machine:
git clone https://github.com/TanoojSeelam/Phishing-URL-Detection-Using-ML.git- Navigate to the project directory:
cd Phishing-URL-Detection-Using-ML- Run the main script:
python main.pyThis script will preprocess the data, train the machine learning model, and evaluate its performance.
In this educational project, the focus is on understanding phishing attacks and their potential dangers rather than achieving high accuracy in detecting them. Therefore, the "results" in this context would entail gaining insights into how phishing attacks work and recognizing common strategies used by attackers.
- Objective: Gain understanding of phishing attacks and their methods.
- Outcome: Identify common phishing strategies, such as deceptive URLs, fake login pages, and social engineering techniques.
- Educational Goal: Recognize the importance of vigilance and skepticism while browsing online, especially when dealing with sensitive information such as IDs and passwords.
As this project is primarily educational, there won't be conventional metrics like accuracy, precision, recall, or F1-score. Instead, the success of this project lies in the knowledge gained by students regarding the dangers of phishing attacks and the importance of cybersecurity practices.
Contributions to this project are welcome! If you have any suggestions for improvement or would like to add new features, feel free to open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.