Capstone
Cybersecurity
Network Intrusion Detection System
Machine-learning anomaly detection over live network traffic
- Python
- Scapy
- XGBoost
- Kafka
- +2
₹5,499₹9,499
4.9/97 reviews
Cybersecurity · Mini Project
Phishing URL Detection
Classify malicious links from their structure, not a blocklist
1-2 weeks build
Overview
A classifier that decides whether a URL is phishing from lexical and host features alone — length, entropy, suspicious tokens, domain age, certificate facts — so it catches links no blocklist has seen yet. Ships with a paste-a-link web form and the full feature-importance analysis.
What makes it stand up
- 01Thirty engineered features with importance rankings
- 02Catches new links a blocklist would miss
- 03Model comparison across four algorithms, honestly reported
- 04Paste-a-URL web demo with confidence and reasoning
- 05Labelled dataset and the notebook that built it
Module breakdown
- 01
Features
Lexical, host and certificate feature extraction.
- 02
Training
Model comparison, tuning and cross-validation.
- 03
Evaluation
The precision-recall trade-off for a security tool.
- 04
Service
Flask API and paste-a-URL checker.
After this, you will be able to
- Explain why recall matters more than accuracy here
- Name your top five features and why they predict
- Show it catching a link that is not on any blocklist
Technology stack
- Python
- scikit-learn
- Flask
- Pandas
- Level
- Mini Project
- Domain
- Cybersecurity
- Build time
- 1-2 weeks
- Difficulty
- Rating
- 4.6 / 5 · 284
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support