AI Protector

Inspiration

The inspiration behind AI Protector came from the increasing risks of phishing attacks, data leaks, and online privacy threats in today's digital world. With growing concerns about cyber threats, I wanted to create a tool that could ensure a safer, more secure browsing experience for users. This led me to explore AI-powered solutions for real-time content filtering and autofill protection.

What it does

AI Protector is a browser extension designed to protect users from online threats, particularly phishing attacks and unsafe autofill forms. It leverages artificial intelligence to provide real-time content filtering and privacy protection while browsing. Here's a breakdown of its core features:

Phishing Detection: It uses AI-powered models to detect phishing websites and warns users when they land on potentially malicious pages.

Autofill Protection: The extension prevents malicious autofill submissions that could lead to data leaks by monitoring and blocking unsafe form interactions.

Real-Time Page Scanning: AI Protector scans web pages in real-time for harmful or unsafe content, alerting users to potential risks on the pages they visit.

Custom Keyword Blocking: It allows users to add keywords they wish to block, ensuring personalized content filtering.

Adaptive Filtering: The extension learns user preferences over time to provide more accurate, real-time adaptive filtering based on the content they encounter.

By integrating AI models for content analysis and privacy protection, AI Protector offers a robust and intelligent way to secure online browsing and safeguard users from various cyber threats.

How we built it

I used a combination of JavaScript, AI models, and cloud storage to build the extension. The backend runs on a Spring Boot framework that connects with the extension’s frontend. The extension uses AI models for content filtering, phishing detection, and real-time scanning.

Challenges we ran into

Integrating AI models with the extension posed significant challenges in terms of performance optimization. Handling cross-origin requests and ensuring smooth communication between the extension and backend was complex. Ensuring scalability and compatibility with different web pages also required extensive testing and refining. Ultimately, the project not only helped me develop new technical skills but also gave me a deeper appreciation for the importance of cybersecurity in our digital lives.

Accomplishments that we're proud of

Advanced AI Integration: Successfully integrated an AI-powered content filtering system capable of identifying phishing websites, unsafe autofill forms, and other malicious content in real-time, providing a layer of proactive protection for users.

Real-Time Scanning & Adaptive Learning: Developed a robust system for real-time page scanning, allowing the extension to instantly assess the safety of websites. Adaptive filtering was also implemented, where the extension learns user preferences and continuously improves its accuracy.

User-Centric Design: Created a simple yet effective user interface that enables easy interaction with complex features, such as keyword blocking and adaptive filtering. This allows users to control their browsing safety with minimal effort.

Privacy Protection Focus: By incorporating autofill protection and customizable settings, AI Protector goes beyond basic security to ensure that users' personal data remains private and protected from exploitation.

Seamless Integration with Browsers: Ensured smooth integration with Google Chrome’s extension system, using modern technologies like JavaScript, Web Assembly, and AI models to enhance performance and deliver a polished user experience.

Real-Time Notifications & Alerts: Implemented real-time alerts and notifications to inform users immediately about security risks, allowing them to make informed decisions while browsing.

What we learned

AI Integration for Browsing Safety: We learned how to effectively leverage AI to provide real-time content filtering and behavior analysis in a browser extension. This required understanding both machine learning models and their practical application in detecting harmful content, such as phishing or data leaks.

Building Scalable and Responsive Systems: Developing a Chrome extension that is both lightweight and powerful required us to focus on optimizing performance, especially when handling large datasets or integrating AI models. We learned how to create responsive systems that can scale as more features or data are added.

User Privacy and Data Security: Privacy was a primary concern throughout the project. We discovered how to balance between providing AI-powered functionality and ensuring user privacy. Techniques like local data storage and encrypted communication ensured that user data was always protected.

Real-Time Web Interactions: Interacting with websites in real-time to scan and analyze content taught us the intricacies of handling dynamic web pages and executing background scripts efficiently. We had to adapt to how content is structured and delivered on modern websites.

UI/UX Design for Security: While building the interface, we learned the importance of making security tools accessible. Providing clear feedback to users in a user-friendly way while ensuring that complex features remained intuitive was challenging but critical.

Handling Complex Tech Stacks: Integrating JavaScript, Node.js, Web Assembly, and machine learning in a single project required careful planning and execution. We became more adept at managing diverse technologies and learning how to make them work together seamlessly.

What's next for AI Protector

Built With

• chromeextensionapi
• chromestorageapi
• css
• firebase
• google-cloud
• html
• java
• javascript
• mashinelearningapi
• node.js
• springboot
• tensorflow
• webassembly