Sentinel Scan

Inspiration

Our inspiration stemmed from the dual objectives of enhancing online security and providing personalized protection for students against potential vulnerabilities. Recognizing the unique browsing habits and needs of students, we aimed to tailor our tool to offer a personalized experience specifically geared towards their online safety and security.

What it does

Sentinel Scan is a cybersecurity tool that thoroughly checks websites for any security weaknesses. Using AI technology, it carefully examines every part of a website to find potential vulnerabilities, such as common issues like Cross-Site Scripting (XSS) and other complicated problems. Plus, it's easy to use, so whether you're a cybersecurity expert, a website manager, or a student, you can easily understand and use it to make your online space safer from cyber threats.

How we built it

We developed a Google Chrome plugin as the backbone of our tool. Additionally, we encountered the challenge of maintaining user sessions without a backend server, which led us to implement Flask to address this issue. Integrating the Neural Network for Sentinel with the browser extension was another key aspect of our development process.

We split up the project into two parts: --Ansh and Alex worked on creating the Neural Network and implementing Flask as our backend server --Justin and Sibaie created the Google Chrome plugin and implemented the use of MongoDB

Google Chrome Plugin: UI Framework Flask: Backend server to host the machine learning model MongoDB: Database for our sign-in information Linux Terminal: Where we trained our Neural Network using Bash, Tensorflow, and Numpy

Challenges we ran into

We faced various challenges throughout the project, notably the difficulty of persisting user sessions without a backend server. This prompted us to incorporate Flask into our solution. Additionally, connecting the Neural Network for Sentinel to the browser extension presented its own set of hurdles. We had to use a FlaskAPI in order for Sentinel to access the Neural Network output. Only then was Sentinel able to display the correct information.

Accomplishments that we're proud of

We're proud of learning so many new technologies and programming languages during this project. One highlight was creating an accurate Neural Network with lots of data in a short amount of time. We also feel accomplished in learning how to use APIs.

What we learned

Our experience with Sentinel Scan taught us a lot about JavaScript/Python capabilities, Neural Networks, Google Extension development, and working with APIs and Mongo DB. These lessons gave us a better grasp of how these technologies work and how they can be used in real-life situations. Additionally, we gained valuable knowledge on using Flask.

What's next for Sentinel Scan

Our next objective is to publish Sentinel Scan on the Google Store, as currently, it operates locally due to being hosted on a local server using Flask. We aim to make it more accessible to users by offering it as a readily available extension on the Google Chrome Web Store.