Binary Entropy Analyzer
Compute Shannon entropy of any binary data to determine if it's encrypted, compressed, or plain text. View byte frequency tables, entropy heatmaps, chi-square statistics, serial correlation, and automatic file type detection. All processing is local and private.
Why Use Our Entropy Analyzer?
Shannon Entropy Calculation
The entropy analyzer computes Shannon entropy (0-8 bits/byte) for any binary data. This fundamental metric measures the information density and randomness of your data, enabling instant classification as encrypted, compressed, or plain text.
Comprehensive Byte Frequency Table
Every byte value (0x00-0xFF) present in your data is counted and displayed in a sortable frequency table. See counts, percentages, hex values, and ASCII representations. Top 10 most and least frequent bytes highlighted for quick analysis.
Entropy Distribution Heatmap & Graph
Visualize how entropy varies across your data with a sliding-window entropy analysis. The entropy analyzer divides your data into blocks and computes local entropy for each, producing a color-coded heatmap and interactive line chart to spot high and low entropy regions.
100% Private - No Data Upload
All entropy analysis happens locally in your browser using JavaScript. Your data never leaves your device. No account required, no tracking, no data stored anywhere. Export results as JSON or CSV for your records.
Common Use Cases for the Entropy Analyzer
Malware & Packer Detection
Security analysts use entropy analysis to detect packed or obfuscated malware. Packed executables show uniformly high entropy (7.0+ bits/byte), while normal executables have structured, varying entropy levels. The entropy analyzer makes this distinction instantly visible.
Encrypted vs Compressed Data Identification
Both encrypted and compressed data have high entropy, but their statistical properties differ. Encrypted data shows near-uniform byte distribution with low serial correlation, while compressed data has subtle structure. Our entropy analyzer computes chi-square statistics and serial correlation to help distinguish them.
File Format & Magic Byte Identification
The entropy analyzer automatically detects common file signatures (magic bytes) including PDF, ZIP, PNG, JPEG, ELF, PE, Mach-O, and GZip. Combined with entropy analysis, this helps identify unknown binary files and verify file type claims.
Capture The Flag (CTF) Challenges
CTF participants use entropy analysis to identify encryption algorithms, detect hidden data in binaries, and classify unknown file formats. The entropy heatmap reveals where encoded payloads may be hidden within otherwise normal data.
Data Integrity & Randomness Testing
Test the quality of random number generators, verify data integrity, and detect non-random patterns in supposedly random data. The chi-square test and serial correlation metrics provide statistical evidence of randomness quality.
Cryptography & Information Theory Education
Students can visually explore how different encryption algorithms, compression methods, and encoding schemes affect byte distributions and entropy. Compare plain text vs encrypted vs compressed versions of the same content.
Understanding Entropy Analysis
What is Shannon Entropy?
Shannon entropy, named after Claude Shannon (the father of information theory), measures the average information content or unpredictability of a data source. In the context of binary data, entropy quantifies how evenly distributed byte values are across the 0-255 range. High entropy (near 8 bits/byte) means all byte values are equally likely — characteristic of encrypted or compressed data. Low entropy means certain byte values dominate — typical of plain text, structured formats, or repetitive data. Our entropy analyzer computes this metric instantly for any input.
How the Entropy Analyzer Works
- Input your data in any supported format — paste hex, Base64, plain text, or upload a binary file. The entropy analyzer accepts all formats and converts them to raw bytes for analysis.
- Byte-level analysis: the analyzer counts every byte, computes Shannon entropy, performs chi-square testing against a uniform distribution, calculates serial correlation between consecutive bytes, and builds a complete byte frequency table — all in your browser.
- Explore the results: review the classification badge, explore the sortable byte frequency table, examine the interactive entropy heatmap and line chart, and check the top and bottom byte lists. Export the full analysis as JSON or CSV for offline use.
Key Metrics Explained
- Shannon Entropy (H): measured in bits per byte (0-8). Natural text typically scores 4.0-5.0, executables 5.0-6.5, compressed archives 7.0-7.9, and encrypted/random data 7.5-8.0. The theoretical maximum is log₂(256) = 8 bits/byte.
- Chi-Square (χ²): a statistical test comparing observed byte frequencies against the expected uniform distribution. Lower values suggest more uniform (random) data. Values below 300 indicate near-uniform distribution consistent with encryption.
- Serial Correlation: measures the dependency between consecutive bytes. Zero means independent bytes (random). Positive values indicate repeating patterns (structured data). Negative values suggest alternating patterns.
- Compression Estimate: the theoretical minimum bits per byte that a perfect compressor could achieve, equal to the entropy value. A value of 4 means the data could theoretically be compressed to 50% of its original size.
Privacy, Security & Usage Notes
The entropy analyzer processes all data entirely in your browser using JavaScript. No data is ever uploaded to any server, stored in any database, or shared with any third party. There is no file size limit — the only constraint is your browser's available memory. For very large files (over 100 MB), the analysis may take a moment but will complete without any server communication. Export your analysis as JSON or CSV to integrate with other tools or keep for your records.
Related Binary Analysis Tools
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Upload or paste code to detect obfuscation techniques including packing, string encoding, eval usage, and more.
Frequently Asked Questions About Entropy Analysis
Shannon entropy measures the average information content or unpredictability of data, expressed in bits per byte (0-8). An entropy of 0 means all bytes are identical (completely predictable), while 8 means all 256 possible byte values appear with equal frequency (completely random). Our entropy analyzer computes this using the formula H = -Σ(p_i × log₂(p_i)) for all byte values present in the data.
High entropy (7.0-8.0 bits/byte) indicates that byte values are distributed nearly uniformly across the 0-255 range. This is characteristic of encrypted data, compressed archives (ZIP, GZIP, PNG), cryptographic keys, or random data. The entropy analyzer also computes chi-square and serial correlation to help distinguish between encrypted (near-perfect uniformity, low correlation) and compressed data (slightly structured, moderate correlation).
Low entropy (0-3.0 bits/byte) means certain byte values dominate the data. This is typical of repetitive data, sparse structures, constant byte sequences, or short headers. Plain text in a single language typically scores 4.0-5.0 bits/byte. Very low entropy (<1.5) indicates either highly repetitive patterns or very short data where statistics are unreliable.
The entropy analyzer supports four input formats: (1) Hex - paste hexadecimal strings with or without 0x prefixes, whitespace is automatically stripped; (2) Base64 - decode standard Base64-encoded data; (3) Plain Text - analyze text as UTF-8 encoded bytes; (4) Binary File Upload - upload any file directly for byte-level analysis. All processing happens locally in your browser.
The sliding window analysis divides your data into overlapping blocks (default 64 bytes per window, 32 byte step) and computes the entropy of each block independently. This reveals how entropy varies across your data - encrypted sections show uniformly high entropy, while plain text sections show lower, varying entropy. The heatmap and line chart make these patterns visually obvious.
The chi-square (χ²) test compares the observed byte frequency distribution against the expected uniform distribution (where each of the 256 byte values appears the same number of times). A low chi-square value (below 300 for typical data sizes) means the distribution is close to uniform - consistent with encryption. A high chi-square value indicates significant deviation from uniformity - typical of structured data or plain text.
Serial correlation measures the statistical dependency between consecutive bytes. A value near 0 means each byte is independent of its neighbors (characteristic of encryption or true randomness). Positive values (0.1-0.9) indicate repeating patterns, common in structured data, text, or executables. Negative values suggest alternating byte patterns. This helps distinguish encrypted data from compressed data, as compression often preserves some byte-to-byte correlation.
Yes, completely. All entropy analysis happens locally in your browser using JavaScript. Your data never leaves your device and is never sent to any server. No account is required, no data is stored, and no tracking occurs. You can safely analyze confidential files, encrypted data, sensitive binaries, or any proprietary content.
The compression estimate equals the Shannon entropy in bits per byte, which represents the theoretical minimum number of bits needed to represent each byte on average. A value of 4.0 means a perfect compressor could theoretically reduce the data to 50% of its original size. Real-world compressors (like gzip or brotli) typically get close to this theoretical limit for most data types.
Yes, the entropy analyzer automatically checks for common file signatures (magic bytes) including PDF (%PDF), ZIP (PK), PNG (‰PNG), JPEG (ÿØ), GIF (GIF), ELF binaries (ELF), PE executables (MZ), Mach-O (feedface/feedfacf), GZip (1f8b), BZip2 (BZ), and WebP (RIFF....WEBP). Combined with entropy analysis and ASCII printable ratio, this helps identify unknown file formats.