Chapter 4 — CyberMentor
Based on: SIH1697 / SIH1703 — Cybersecurity Learning and Awareness (converted problem)
Skills required: Cybersecurity training, Education technology, AI/ML for education, Chatbots, Python, NLP basics
Project Description
CyberMentor is an AI-powered cybersecurity awareness and training bot designed to deliver interactive learning experiences. It simulates real-world attack scenarios, delivers curated educational content, quizzes users, and provides personalized learning paths to elevate cybersecurity skills. The chatbot supports multi-channel deployment including web, mobile, and messaging platforms.
Tech Stack & Tools
- Python (backend, chatbot logic)
- NLP libraries (spaCy, NLTK, Hugging Face transformers)
- Flask/FastAPI (API server)
- React or Vue (frontend interface)
- Chatbot frameworks (Rasa, Dialogflow, Microsoft Bot Framework)
- Databases (SQLite/PostgreSQL for user progress)
- ML models for content personalization and quiz generation
Week-wise Roadmap (6–8 Weeks)
Week 1 — Requirement Gathering & Content Mapping
- Define target audience and cybersecurity skill levels.
- Collect and structure educational content: theory, attack examples, best practices.
- Design learning modules and assessment strategy.
- Deliverable: Content map and syllabus document.
Week 2 — Chatbot Framework Setup & Basic Flows
- Set up chatbot framework backend and development environment.
- Implement core conversational flows for greetings, content delivery, and quiz handling.
- Prepare a webhook API that interfaces with the chatbot engine.
- Deliverable: Basic chatbot prototype with static content flows.
Week 3 — Dynamic Content & Personalized Learning Paths
- Integrate NLP for intent recognition and entity extraction.
- Build models to assess user responses and adapt quiz difficulty.
- Implement user authentication and progress tracking database schema.
- Deliverable: Personalized learning module with user state management.
Week 4 — Multi-Channel Integration and Frontend
- Develop and deploy a React or Vue frontend web interface.
- Integrate chatbot with messaging platforms (Slack, Telegram).
- Build dashboard for administrators to monitor learner progress and content usage.
- Deliverable: Functional multi-channel chatbot with simple user interface.
Week 5 — Quiz Engine & Gamification
- Develop dynamic quiz generation with feedback and scoring.
- Add gamification elements like badges, levels, and leaderboards.
- Improve chatbot's conversational abilities using pre-trained language models.
- Deliverable: Engaging quiz system integrated into chatbot.
Week 6 — Testing & User Feedback
- Conduct usability testing with representative users.
- Collect and analyze feedback to refine flows, content, and UI/UX.
- Address bugs, improve response accuracy, and enhance performance.
- Deliverable: Tested and user-validated chatbot system.
Week 7 — Documentation and Deployment
- Prepare user manuals and admin guides.
- Containerize the application for easy deployment (Docker/Kubernetes).
- Deploy on a cloud instance with monitoring set up.
- Deliverable: Production-ready chatbot with deployment instructions.
Week 8 — Advanced Features & Extensions (Optional)
- Add natural language generation for richer content delivery.
- Integrate live instructor support or chatbot handoff.
- Build analytics reports for deeper learning insights.
- Deliverable: Enhanced chatbot with extended capabilities.
Testing & Deliverables
- Use sample user cohorts to test chatbot responsiveness and learning efficacy.
- Validate personalized learning paths and quiz feedback mechanisms.
- Final deliverables include: complete code repository, deployment scripts, user and admin documentation, and demo videos.