Quasar

Empowering Security, Safeguarding Futures: Your Shield in the Digital Realm.

Comment

Inspiration

What it does

The android application is used for automatically parsing the SMS messages and detecting as spam or ham. Scanning files and URLs. Extraction of metadata. The web application is also used for static code analysis and malware scanning which is also integrated with various cybersecurity tools.

How we built it

We built a web application using ReactJS and TailWind and a mobile application with React Native, TailWind and MongoDB

Challenges we ran into

  1. We were unable to incorporate security standard SQL injection tools. But we somehow managed to integrate a basic SQL injection code with very limited functionalities.
  2. We were trying to integrate OpenStreetMap API for picturizing the GPS location of the file with the respective GPS meta data, but due to lack of documentation for OpenStreetMap we lost loads of time due to trail and error while incorporating the API in our application.

Innovations

Users no longer need to search for and gather multiple tools based on their specific needs; our application seamlessly integrates various security scanning functionalities, serving as an all-in-one solution for security assessments.

We integrated an AI model that uses Random Forest in the android application to detect SMS Spams. We managed to train the model with an accuracy of 97.29 and F1 Score of 87.72. This model will be used in our SPAM detection application which automatically parses the messages of the user and classifies as spam or ham.

Accomplishments that we're proud of

We were delighted to incorporate MongoDB in our project with limited knowledge this DBMS. We were also relieved that we somehow managed to include OpenStreetMap API into our project with very little to no documentations in their websites

What we learned

MongoDB Integration of OpenStreetMap API using REST API

What's next for CyberSentinel

Since users use the application to scan their files and websites for malwares which is stored in MongoDB, later the information can be used to generate user analysis reports with visualization for easy understanding.

Share this project:

Updates