In the ever-evolving landscape of cyber threats, phishing stands as one of the most commonly utilized social engineering attacks. Through these cunning maneuvers, malicious actors target unsuspecting online users, manipulating them into revealing sensitive and confidential information, which is then exploited for fraudulent purposes. To counteract this threat, it is crucial for users to possess the awareness and tools necessary to identify and avoid phishing websites.
The primary goal of this project is to mitigate the risk of falling victim to phishing attacks by harnessing the power of machine learning and deep neural network algorithms. By training models on a carefully curated dataset containing both phishing and benign URLs, we aim to predict the authenticity of websites, effectively detecting potential phishing attempts in their early stages.
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Dataset Creation: Gathering a comprehensive dataset encompassing both phishing and benign URLs, which serve as the foundation for training our models.
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Feature Extraction: Extracting essential URL and website content-based features from the dataset, enabling our models to make accurate predictions.
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Model Selection and Training: Implementing a range of machine learning algorithms, including Decision Trees, Random Forest, XGBoost, SVM, and Logistic Regression. These models are meticulously trained on the dataset.
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Performance Evaluation: Conducting rigorous performance evaluations for each model to assess their accuracy and effectiveness.
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Enhancing Accuracy: Exploring advanced techniques such as XGBoost, CatBoost, and neural networks to further improve the accuracy of our predictions.
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Data Analysis: Performing univariate, bivariate, and multivariate data analyses to gain insights and identify correlations using tools such as heatmaps and correlation matrices.
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Model Implementation: Utilizing a variety of machine learning models, such as SVM, Random Forest, Decision Trees, and Logistic Regression, to train and fine-tune our detection algorithms.
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Performance Comparison: Comparing the performance of each model and selecting the most effective one for phishing website detection.
Our project aims to empower users with the tools and knowledge needed to identify and avoid phishing websites effectively, thus enhancing online security and reducing the risks associated with cyber threats.
- Sainik Khaddar
For inquiries or collaboration opportunities, please feel free to reach out to us:
Email: sainikwarror132@gmail.com
This project is licensed under the MIT License.