A behavior-based password security analysis tool that evaluates password strength using entropy modeling, structural pattern detection, and attacker-style heuristics inspired by real-world credential attacks.
It simulates how attackers think by identifying predictable human password behaviors rather than relying only on simple dictionary checks.
This project is designed from an attacker's perspective.
Instead of simply checking password rules, it models how real-world attackers evaluate password weakness using:
- entropy estimation
- predictable human behavior patterns
- structural password analysis
- Entropy-based password strength estimation
- Detection of weak patterns (common passwords, keyboard sequences, repeated characters)
- Sequential pattern detection (e.g., abcd, 1234)
- Structure analysis (word + numbers, predictable formats)
- Risk scoring system (0–100 scale)
- Smart security recommendations
- Rich CLI output for better readability
Estimates randomness based on character set complexity and password length.
Flags known weak patterns such as:
- Common passwords (admin, password)
- Keyboard patterns (qwerty, asdfgh)
- Sequential patterns (abcd, 1234)
Detects predictable formats like:
- Letters + numbers (Meet123)
- Word + symbol + numbers (Meet@123)
Final score is calculated using:
- Entropy contribution (primary factor)
- Length bonus (secondary factor)
- Pattern penalties (risk reduction)
python -m src.mainScore: 18/100
Strength: Very Weak
Risk Factors:
- common pattern: password
- sequential pattern detected: 1234
Score: 92/100
Strength: Very Strong
No major issues detected.
src/
├── analyzer.py
├── main.py
│
assets/
├── weak.png
├── strong.png
│
requirements.txt
README.md
.gitignore
Most password checkers rely on static rule validation (length, digits, symbols).
This project goes further by simulating attacker reasoning:
- How predictable is the password structure?
- Does it resemble human-generated patterns?
- How much entropy does it realistically contain?
This makes it closer to real-world security evaluation systems used in defensive security engineering and penetration testing environments.
- Python 3
- Regex
- Mathematical entropy modeling
- Rich CLI formatting
Cybersecurity portfolio project focused on password analysis, attacker simulation, and defensive security modeling.
Built as part of a cybersecurity learning path focused on defensive security engineering and attack simulation models.