CLIENT SIDE PROTECTION AGAINST WEB SPOOFING ATTACKS WITH PHISH CATCHER:THE ADVANCED XGBOOST ALGORITHM

Abstract

Cyber security confronts a tremendous challenge of maintaining the confidentiality and integrity of user’s private information such as password and PIN code. Billions of users are exposed daily to fake login pages requesting secret information. There are many ways to trick a user to visit a web page such as, phishing mails, tempting advertisements, click-jacking, malware, SQL injection, session hijacking, man-in-the-middle, denial of service and cross- site scripting attacks. Web spoofing or phishing is an electronic trick in which the attacker constructs a malicious copy of a legitimate web page and request users’ private information such as password. To counter such exploits, researchers have proposed several security strategies but they face latency and accuracy issues. To overcome such issues, we propose and develop client-side defence mechanism based on machine learning techniques to detect spoofed web pages , and protect users from phishing attacks. As a proof of concept, a Google Chrome extension dubbed as Phish Catcher, is developed that implements our machine learning algorithm that classifies a URL as suspicious or trustful. The algorithm takes four different types of web features as input and then random forest classifier decides whether a login web page is spoofed or not. To assess the accuracy and precision of the extension, multiple experiments were carried on real web applications. The experimental results show remarkable accuracy of 98.5% and precision as 98.5% from the trials performed on 400 classified phished and 400 legitimate URLs. Furthermore, to measure the latency of our tool,we performed experiments over forty phished URLs. The average recorded response time ofPhish Catcher was just 62.5 milliseconds.

Specification

Field of Invention: Machine Learning

Background Art including citations of prior art: There is no application openly available to the public that to avoid the web spoofing attacks.

Objective of invention (the invention's objectives and advantages, or alternative embodiments of the invention):

The Innovation aimed to provide an efficient, privacy-focused, and adaptable defense against web spoofing attacks, enhancing online security for users.

The objectives of Innovations are:

1. Real-Time Phishing Detection: Implement a client-side solution that can identify and block phishing attempts in real time. This helps users avoid falling victim to spoofed websites that aim to steal sensitive information like passwords, credit card details, and other personal data.

2. Use of Advanced Machine Learning: Utilize the XGBoost algorithm, known for its high performance in classification tasks, to accurately detect phishing websites. XGBoost can analyze various features of a website (like URL patterns, metadata, and content structure) and determine if it's a legitimate site or a spoofed one.

3. Client-Side Deployment: Ensure the solution operates directly on the user's device, providing protection without relying on external servers. This improves privacy and speed, as the detection process happens locally.

4. Improved Accuracy and Reduced False Positives: By using a sophisticated

machine learning model, the innovation aims to improve the accuracy of

phishing detection, minimizing the chances of false positives (legitimate sites t being . flagged as phishing) and false negatives (phishing sites not being detected).


User-Friendly Experience: Provide seamless integration within browsers or other applications, ensuring that users receive alerts or warnings without disrupting . their browsing experience.

Summary of Invention:

The innovation "Client Side Protection against Web Spoofing Attacks with Phish Catcher: The Advanced XGboost Algorithm" represents a significant advancement in online security by providing an effective, real-time solution for detecting phishing attacks. By leveraging the power of the XGBoost machine learning algorithm, this client-side approach offers high accuracy, speed, and adaptability, ensuring users are safeguarded from malicious sites without compromising their privacy. The system's ability to function seamlessly across different platforms and its adaptability to emerging phishing tactics make it a robust and reliable tool for modern web security needs. Additionally, by operating locally on users' devices, it reduces dependency on external servers and minimizes latency, delivering protection where it's needed most-directly on the user's browser.

Through this innovative solution, users can browse with greater confidence, benefiting from enhanced security, privacy, and awareness, ultimately reducing the risks associated with web spoofing and phishing attacks.



Detailed description of the invention:

The Innovation "Client Side Protection Against Web Spoofing Attacks With Phish

Catcher: The Advanced XGboost Algorithm" is designed to develop a real-time, client-side security solution that detects and prevents phishing attacks. Phishing is a common form of cybercrime where attackers create spoofed websites to trick users into revealing sensitive information, such as passwords, credit card numbers, and other personal data. This project aims to counteract these threats by employing an advanced machine learning algorithm XGBoost to accurately identify and block phishing attempts before they can cause harm.

Key Components:

1. Machine Learning-Based Detection:

o The core of the system relies on the XGBoost algorithm, known for its efficiency and high accuracy in classification tasks. It analyzes various features of a website, including URL patterns, metadata, domain information, and HTML content, to determine if a site is legitimate or fraudulent.

o By training the model on large datasets of known phishing and legitimate sites, the system learns to recognize the subtle patterns and tactics used by attackers.

2. Client-Side Implementation:

o Unlike traditional server-based solutions, this project focuses on client-side deployment, meaning the detection engine operates directly on the user's device. This approach provides faster response times and preserves user privacy, as sensitive data does not need to be sent to external servers.

o The system can be integrated as a browser extension or standalone
application, making it versatile across different platforms.-and devices.


Real-Time Protection:
o The solution provides real-time monitoring and alerts, warning users instantly if they attempt to visit a suspicious or potentially harmful site. This proactive protection helps prevent users from entering their personal information on phishing sites.

o The systerrTs design prioritizes low latency and minimal impact on device performance, ensuring that security measures do not interfere with the user's browsing experience.

4. Adaptability and Continuous Learning:

o The threat landscape of phishing attacks is constantly evolving, with attackers developing new methods to bypass traditional detection techniques. This project addresses that by incorporating adaptive learning, allowing the model to update regularly based on new data and emerging phishing tactics.

o This ensures the system remains effective over time, even as phishing methods change.

5. Cross-Platform Compatibility:

o To reach a wider audience, the project emphasizes compatibility across multiple operating systems (Windows, macOS. Linux) and browsers (Chrome, Firefox, Safari, etc.). This flexibility allows users to benefit from consistent protection regardless of the device or software they are using.

o The design also focuses on ease of use, with a user-friendly interface that makes it simple for users to install, configure, and interact with the system.

o In addition to blocking phishing attempts, the system also educates users
by providing information on why a site was flagged as suspicious. This
helps users learn about common phishing tactics, enhancing their ability
to recognize and avoid such attacks in the future.
XGBoost Algorithm:

XGBoost (Extreme Gradient Boosting) is a powerful machine learning algorithm that is widely used for classification and regression tasks. It is particularly well-known for its speed and performance, especially in structured data environments like those often found in business and academic applications.

Gradient Boosting Framework: XGBoost is based on the gradient boosting framework, which builds an ensemble of decision trees in a sequential manner. Each new tree is trained to correct the errors made by the previous trees in the ensemble. Boosting: Boosting is an ensemble technique that combines the predictions of multiple weak learners (in this case, decision trees) to create a stronger model. In XGBoost. trees are added iteratively, and each new tree focuses on the residual errors of the combined previous trees.

Features of XGBoost

1. Regularization:

o XGBoost includes LI (Lasso) and L2 (Ridge) regularization, which help prevent overfitting and improve model generalization.

2. Handling Missing Values:

o The algorithm can automatically handle missing data by learning the best direction to take for missing values during training.
o XGBoost can leverage multiple CPU cores to perform parallel processing during tree construction, significantly speeding up the training process.

4. Tree Pruning:

o It uses a depth-first approach for tree growth, which allows for more efficient pruning of trees based on a maximum depth parameter.

5. Learning Rate (Shrinkage):

o XGBoost incorporates a learning rate parameter that controls how much each tree contributes to the final prediction. Lower learning rates can lead to better performance at the cost of increased training time.

How XGBoost Works
1. Initialization:

o Start with an initial prediction, usually the mean of the target variable for regression tasks.

2. Iterative Process:

o For a specified number of boosting rounds (iterations):

■ Calculate the residuals (errors) between the current predictions and the actual target values.

■ Fit a new decision tree to these residuals. The new tree focuses on correcting the mistakes of the combined model of previous trees.

• Update the predictions by adding the output of the new tree, scaled by the learning rate.

3. Final Prediction:

o The final model is a weighted sum of all the trees, where each tree's contribution is determined by the learning rale.

Advantages of XGBoost

High Performance:

o XGBoost often outperforms other machine learning algorithms in terms of accuracy and speed, making it a popular choice in data science competitions.

2. Flexibility:

o The algorithm supports various objective functions, including regression, classification, and ranking, allowing it to be applied to a wide range of problems.

3. Robustness:

o It is robust to overfitting due to its regularization techniques and ability to handle different types of data distributions.

4. Community Support and Documentation:

o XGBoost has a strong community and extensive documentation, making it easier for practitioners to implement and troubleshoot.

The Innovation offers a promising approach to protecting users from phishing attacks through client-side machine learning, it faces limitations related to data quality, resource consumption, model adaptability, and the challenges of maintaining accuracy. Addressing these limitations will be crucial to developing a reliable, scalable, and user-friendly security solution.




Claims:
1) Client Side Protection Against Web Spoofing Attacks With Phish Catcher: The advanced XGboost Algorithm designed to develop a real-time, client-side security solution that detects and prevents phishing attacks.


Using XGBoost and Machine Learning Algorithm.

3) As claimed in Claim 1. the application uses XG Boost Model.

4) As claimed in Claim 1, the application uses Random Forest Classifier to delect whether the web page is spoofed or not

5) As claimed in Claim 2. a Google Chrome extension dubbed as Phish Catcher, is developed that implements our machine learning algorithm that classifies a URL as suspicious or trustful

6) As claimed in Claim 3, the application uses XGBoost Model along with supervised classification algorithm.

7) As claimed in Claim 6, the application for Client Side Protection Against Web Spoofing Attacks With Phish Catcher: The advanced XGboost Algorithm and Machine Learning is accuracy of 98.5% and precision as 98.5% from the trials performed on 400 classified phished and 400 legitimate URLs.

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