socialshield

Inspiration

With the rapid growth of social media platforms such as WhatsApp, Instagram, and Telegram, online scams, fake accounts, phishing attacks, identity theft, and financial fraud are increasing. We were inspired to build SocialShield to provide users with an intelligent security layer that can analyze suspicious content and user behavior and help them identify potential online threats.

What it does

SocialShield is an AI-based social media security system that helps detect and analyze suspicious activities.

It can:

Detect scam and suspicious messages using NLP Extract hidden text from images using OCR Detect suspicious/fake accounts using profile and activity information Analyze abnormal user behavior Detect suspicious login patterns Calculate a 0–100 risk score Classify activities as Safe or Suspicious Provide security warnings and alerts Analyze screenshots for potential scams Provide security and threat monitoring information

How we built it

SocialShield is developed as a web-based application using:

Frontend: React.js, HTML, Tailwind CSS Backend: Node.js & Express.js Database: MongoDB AI/ML: NLP, OCR, behavioral analysis and heuristic risk scoring Authentication: JWT Development: VS Code / IntelliJ IDEA API Testing: Postman

The system collects user/profile/activity information, processes textual and image-based content, performs risk analysis, and generates a security result for the user.

Challenges we ran into

Detecting scams hidden inside normal-looking messages. Extracting and analyzing text from images and screenshots. Identifying suspicious behavior from multiple activity patterns. Designing a reliable risk-scoring mechanism. Handling different types of social-media threats in one system. Maintaining secure authentication and protecting user data. Working with limited access to real social-media APIs.

Accomplishments that we're proud of

We are proud that SocialShield combines multiple security techniques into a single platform instead of focusing on only one type of threat.

Our major accomplishments include:

NLP-based scam message detection OCR-based screenshot scam analysis Fake account detection Behavioral analysis Login activity monitoring Heuristic risk scoring Real-time threat alerts Security health monitoring Spam reporting and suspicious-user analysis

What we learned

Through SocialShield, we learned how AI techniques such as NLP, OCR, behavioral analysis, and risk scoring can be combined to solve real-world cybersecurity problems.

We also learned the importance of data preprocessing, feature selection, secure authentication, API integration, database management, and testing when developing an AI-based security application.

Most importantly, we learned that effective cybersecurity requires multiple layers of detection rather than relying on a single algorithm.

What's next for socialshield

Our future goal is to make SocialShield more intelligent, real-time, and scalable.

We plan to add:

Advanced Machine Learning and Deep Learning models Real-time social-media API integration More accurate fake-account detection Advanced AI-based fraud detection Mobile applications for Android/iOS Multilingual NLP including Marathi and Hindi More advanced behavioral analysis Real-time threat intelligence Integration with additional social-media platforms Improved automated security alerts

Built With

• analysis
• behavioral
• express.js
• heuristic
• jwt
• mongodb
• natural-language-processing
• node.js
• ocr
• react.js
• rest
• risk
• scoring