InternShield – AI Powered Internship Scam & Fake Job
Every year thousands of students in India fall into fake internship and job scams. Some pay registration fees. Some submit personal documents. Some work for months and never get paid. And honestly, most of them don’t even realize they were scammed until it’s too late.
That’s exactly why we built InternShield — an AI-powered internship scam detection system designed especially for students.
Project tutorials, coding guides & placement tips for students.
Flow
User → Paste JD → Analysis Engine → Score → Save in DB → Show result
Admin → Manage rules/keywords → Impacts scoring immediately🚨 Problem Statement
Students often receive internship offers through WhatsApp, Telegram, Instagram, or random emails. These offers usually look professional but contain hidden red flags like:
- Unrealistic salary claims
- Generic Gmail email IDs
- Fake company names
- Urgent payment requests
- Suspicious wording patterns
There is no simple platform where students can paste a job description and instantly know whether it is safe or risky.
💡 What Is InternShield?
InternShield is an AI-based web application that analyzes internship or job descriptions and calculates a scam probability score.
Students simply paste the job description and the system analyzes:
- Salary pattern realism
- Suspicious scam keywords
- Email and domain trust level
- Overall risk probability
The system then displays a risk meter:
- 🟢 Low Risk
- 🟡 Medium Risk
- 🔴 High Risk
🔥 Why This Project Is Unique
- Not a common resume analyzer project
- Not just another job portal
- Highly relevant in India
- Easy to explain in viva
- Looks advanced but uses explainable AI logic
This project can easily impress external examiners because it solves a real-world student problem.
⚙️ Core Features (MVP)
1. Job Description Analyzer
Students paste internship details and the system extracts key patterns.
2. Salary Scam Detector
Detects unrealistic salary claims using rule-based pattern logic.
3. Suspicious Keyword Scanner
Flags words like “registration fee”, “urgent joining”, “limited seats”, etc.
4. Email & Domain Validation
Identifies free email domains and risky patterns.
5. Scam Probability Score
Generates a score between 0–100 with explanation.
6. Admin Dashboard
Tracks total scans, high-risk cases, and keyword frequency analytics.
🛠 Technology Stack
- Backend: Python (Flask)
- Frontend: HTML, CSS, JavaScript, Bootstrap
- Database: SQLite
- Logic: NLP keyword scoring + rule-based risk analysis
🔥Scam Score Logic (Explainable AI)
This is your viva weapon (simple but sounds powerful)
Score components (Total = 100)
- Keyword Risk (0–50)
- Sum of matched keyword weights
- Cap at 50
- Salary Risk (0–30)
- Example rules:
- “₹50,000/week internship” → high risk
- “₹1,50,000/month for fresher without skills” → medium risk
- Add points based on thresholds
- Example rules:
- Domain Trust (0–20)
- Free email domains used → +10
- Email domain mismatch with website → +10
- Blacklisted domain → +20 (cap)
Risk Level
- 0–30: Low Risk
- 31–60: Medium Risk
- 61–100: High Risk
🎓 Perfect For Final Year Students
This project is ideal for:
- Computer Science students
- BCA / MCA students
- IT engineering students
- Cybersecurity domain
It is easy to demonstrate, easy to explain, and has strong real-world value.
📺 Want Complete Source Code + Documentation?
If you are a final year student and want a ready-to-run project (with proper documentation, DB setup, admin dashboard, and full UI), then InternShield (AI Internship Scam Detection System) is available as a complete package.
✅ Full Flask/Django project (as per your need)
✅ Scam detection engine (keyword + salary + domain/email validation)
✅ SQLite/MySQL database setup
✅ Admin dashboard (manage keywords, view scans, analytics)
✅ Report + PPT + Viva Q&A included
✅ Installation & Run Guide (step-by-step)
Get the full project package from updategadh.com and save your time. Because last-minute project stress is real 😅
Most Viewed Posts
🚀 Future Enhancements
- Blacklisted domain database
- Offer letter PDF upload scanning
- IP tracking system
- Chrome extension integration
- Machine learning enhancement
3 strong viva questions
1. How does InternShield calculate the scam probability score?
Answer:
InternShield uses a rule-based scoring system. It analyzes suspicious keywords, unrealistic salary patterns, and email/domain trust levels. Each factor is assigned a weight, and the total score is calculated out of 100. Based on the final score, the system classifies the internship as Low, Medium, or High Risk. This makes the AI logic explainable and transparent.
2. Why did you choose rule-based AI instead of a heavy machine learning model?
Answer:
We selected a rule-based approach because it is more explainable and suitable for academic purposes. In scam detection, transparency is important. The examiner can clearly understand how each rule contributes to the final score. Also, rule-based systems are lightweight, faster to deploy, and easier to maintain compared to complex ML models.
3. How can this system be improved in the future?
Answer:
In future versions, InternShield can integrate machine learning models trained on real scam datasets. It can also include API-based domain reputation checking, blacklist databases, PDF offer letter scanning, and even a Chrome extension for real-time internship scam detection.
📌 Final
InternShield is not just a college project. It is a real-world solution that can genuinely help students avoid scams. With increasing online fraud cases, such systems are becoming extremely important.
If you are looking for a unique, practical, and high-impact final year project, InternShield is a strong choice.
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