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Information entropy measures the average uncertainty (information content) per symbol. Higher entropy means more randomness/unpredictability. Maximum entropy occurs when all symbols are equally likely.
H = -Σ p(x) × log2(p(x)); where p(x) = frequency(x) / total_symbols; Max H = log2(alphabet_size)Text entropy helps detect: encrypted/compressed data (high entropy ~8 bits/byte), natural language (3.5-5 bits/char for English), passwords (should be >50 bits total), and data compression potential.
Updated: July 2026
Calculate entropy of a password to assess its brute-force resistance.
→ Entropy: ~3.7 bits/char, total: ~48 bits (moderate strength; 80+ bits recommended)
Check if a file block is plaintext or encrypted by measuring its byte entropy.
→ Entropy: ~7.9 bits/byte → likely encrypted/compressed (plaintext would be ~4-5)
Per-character entropy (bits/char) measures randomness density. Total entropy (bits/char × length) measures brute-force resistance. A short random password has high per-char but low total entropy.
Entropy measures symbol distribution uniformity, not true randomness. A repeating pattern "abcabc..." has moderate entropy but is completely predictable. Context and pattern analysis are also needed.
Calculate Shannon entropy of text data. Measure randomness and information density for passwords, compression analysis, and cryptography study. It applies the shannon entropy (H = -Σ p(x) × log2(p(x)); where p(x) = frequency(x) / total_symbols; Max H = log2(alphabet_size)). For example: evaluate password strength — Calculate entropy of a password to assess its brute-force resistance.