Calculate Shannon entropy for any file in your browser. Get an 0-8 score, per-1KB entropy graph, and byte distribution to spot packed or encrypted data.
This entropy analyzer measures the Shannon entropy of any file you give it and reports a single score between 0.0 and 8.0, alongside a per-section entropy graph and a byte-frequency distribution. Drop a binary on the page — an executable, a firmware image, a document, an archive, a certificate blob, a memory dump — and the analysis appears in a second or two. The file is read locally with the browser’s FileReader API and analysed in JavaScript on your own machine: nothing is uploaded to a server, nothing is stored, and no account is required. Files up to 50 MB are supported.
It is built for malware analysts triaging an unknown sample, incident responders looking at a suspicious dropper, reverse engineers hunting for embedded payloads, CTF players who need to know whether a blob is compressed or encrypted, and anyone teaching or learning information theory who wants a real file behind the formula.
Shannon entropy quantifies the average information content — the unpredictability — of a stream of symbols. For a file, the symbols are byte values from 0 to 255, and the entropy in bits per byte is:
H = −∑ p(x) log2 p(x)
where p(x) is the probability of byte value x, estimated as its observed count divided by the total number of bytes. The sum runs over every byte value that actually occurs; values with zero occurrences contribute nothing, which is why the tool skips them rather than evaluating log2(0).
Because there are 256 possible byte values, the theoretical maximum is log2(256) = 8.0 bits per byte, reached only when all 256 values are exactly equally likely. The minimum is 0.0, reached when the file consists of a single repeated byte.
Take a 1,000-byte file made up of only two byte values: 0x41 (“A”) appearing 750 times and 0x42 (“B”) appearing 250 times.
Change the split to 500/500 and every term becomes −0.5 × log2(0.5) = 0.5, giving H = 1.0 exactly — one bit per byte, because a fair coin flip is worth precisely one bit. Now spread the same 1,000 bytes evenly over all 256 possible values and H climbs to very nearly 8.0. The score is not about how big the file is or what it contains; it is about how evenly the byte values are spread.
The tool applies these bands, which reflect long-standing practice in binary triage rather than any formal standard:
High entropy is not evidence of malice. A ZIP archive, a JPEG, an MP4, a PNG, an installer, a signed certificate bundle, and a random key file all score above 7.0 because compression and encryption both work by removing predictability. Every one of them is perfectly benign. What entropy gives you is a fast, cheap signal about the form of the data, not its intent. Treat a high score as a reason to look closer, never as a verdict.
Runtime packers such as UPX, ASPack, PECompact, Themida, and VMProtect compress or encrypt the original code and prepend a small stub that unpacks it in memory at launch. The result is a distinctive shape: a low-to-moderate entropy stub followed by a long high-entropy region. On the section chart this shows up as a flat stretch around 5.0–6.0 that jumps to a plateau near 7.5–8.0 and stays there. A legitimate unpacked binary usually looks more uneven, because import tables, string tables, resources, and relocation data each have their own characteristic entropy. Packing is used by commercial software protection as often as by malware — it is a property to note in a report, not an accusation.
A short, sharp entropy spike inside an otherwise low-entropy file is one of the most useful findings the section chart produces. An encrypted configuration blob appended to an installer, a compressed second-stage payload glued to the end of a document, or data hidden in the trailing bytes after a valid image all produce a localised jump. The tool also computes the standard deviation of section entropies and warns when it exceeds 2.0, which flags files whose content is unusually mixed. Note the section number where the spike occurs, multiply by 1,024 to get the byte offset, then go and look at those bytes directly — the hex editor is the natural next step, and the file magic number checker will tell you whether the region starts with a recognisable file signature.
Entropy is the standard way to talk about how hard a secret is to guess. Feed a file of generated key material to the analyzer: a well-seeded random key should score extremely close to 8.0, and anything materially below that in a file that is supposed to be pure randomness suggests a broken generator, an encoding layer such as base64 or hex sitting on top of the raw bytes, or repeated blocks. Be careful with the units: password-strength discussion normally uses entropy in bits for the whole secret (roughly log2 of the number of equally likely possibilities), whereas this tool reports bits per byte. A 16-byte key at 8.0 bits per byte carries 128 bits total. For evaluating human-chosen passwords rather than key files, the password strength checker and secure password generator are the right instruments.
Obfuscated JavaScript, PowerShell, or VBScript sits in an awkward middle band. It is still text, so it cannot reach the 7.5+ of true encryption, but long base64 or hex-encoded strings and randomised identifier names push it well above the 4.0–4.7 typical of readable source. Entropy in the 5.5–6.5 range for a plain-text script is a strong hint that something is encoded rather than written. Use it as a filter across a directory of scripts, then decode the suspicious ones with the malware deobfuscator or the base64 encoder and decoder.
Entropy is a statistic over byte frequencies and nothing more. It is blind to order: shuffling every byte in a file leaves the score completely unchanged, so it cannot distinguish real ciphertext from a well-shuffled ordinary file. It says nothing about which algorithm produced the data. It is unreliable on very small inputs — a 200-byte file simply does not contain enough samples to estimate 256 probabilities, which is why the tool flags files under 1 KB. And because sections are cut at fixed 1 KB boundaries rather than at parsed structure boundaries, a section that straddles the join between two regions averages both. For PE files, per-section entropy computed from the real section table is more precise than fixed chunking; treat this tool as fast triage that tells you where to point a full-featured analyser.
It depends entirely on the file type. Around 4.0–4.7 is normal for plain text, 5.5–6.5 for compiled machine code, and 7.5–8.0 for compressed or encrypted content such as ZIP, JPEG, MP4, or a key file. There is no universally “good” number — what matters is whether the score matches what the file claims to be.
No. Compression and encryption both raise entropy, and both are overwhelmingly used for legitimate purposes. Archives, media files, installers, and certificate bundles all score above 7.0. High entropy means the data is unpredictable, which justifies a closer look but is never on its own an indicator of compromise.
No. The file is read with the browser’s FileReader API and every calculation runs in JavaScript on your own device. Nothing is transmitted, stored, or logged, which makes it safe to analyse samples you are not permitted to send to a third-party service.
The unit is bits per byte. A byte can hold 256 distinct values, and log2(256) = 8, so eight bits of information per byte is the ceiling. Tools that report a percentage are simply dividing the same number by 8.
Up to 50 MB. Larger files are rejected because the whole buffer is held in browser memory. If you need to check something bigger, analyse a representative slice — the headline score converges quickly on large uniform data.
1,024 bytes is large enough to give a stable frequency estimate and small enough to localise a spike to within a kilobyte of the file. Files large enough to produce more than 1,000 sections are downsampled to evenly spaced points purely so the chart renders; the headline entropy is always computed over every byte.
No. Any competent cipher produces output statistically indistinguishable from random, so AES, ChaCha20, and a well-implemented stream cipher all land at essentially 8.0. Identification has to come from headers, key sizes, block-length artefacts, or context.
Very likely packed, compressed, or largely made of embedded compressed resources. Confirm by checking for packer section names such as UPX0 and UPX1 in the section table, looking at the entry point, and examining the import table — a packed binary typically imports only a handful of functions like LoadLibrary and GetProcAddress. Entropy narrows the question; it does not answer it.
Base64 maps arbitrary data onto 64 printable characters that are used fairly evenly, whereas English text leans heavily on a couple of dozen letters plus the space character. The encoded form is 33% larger and yet more uniform in its byte usage, so the per-byte entropy rises even though no information was added.
It shows the 50 most frequent byte values and their counts. A near-flat bar chart means the values are used uniformly, which points to compression or encryption. Sharp peaks at 0x00, 0x20 (space), or the ASCII letter range identify padding, text, and structured data respectively — a useful sanity check when the headline score is ambiguous.
Shannon entropy, developed by Claude Shannon in 1948, measures information density or randomness within a dataset. For binary files, the formula calculates entropy on a scale of 0 to 8 bits per byte:
H(X) = -Σ p(xᵢ) × log₂(p(xᵢ))
Where H(X) is the entropy in bits per byte, p(xᵢ) is the probability of byte value i occurring, and the sum runs across all 256 possible byte values (0x00 to 0xFF).
Binary files contain sections with distinct purposes. Analyzing entropy per section reveals anomalies that whole-file entropy might miss.
Look for sudden entropy spikes—jumps of > 2.0 indicate transitions from normal code to encrypted data. A typical dropper pattern shows: normal headers (5.2-5.8), encrypted payload (7.8-8.0), then decryption stub (5.5-6.0).
The byte distribution histogram shows how uniformly byte values are distributed:
High entropy doesn't automatically indicate malware. Legitimate high-entropy files include:
Differentiation clues: File extension matches content, valid digital signatures, appropriate section names, and presence of readable metadata all suggest benign high entropy.
Sophisticated attackers employ entropy-lowering techniques:
Entropy analysis should always be combined with static analysis, behavioral monitoring, and signature matching for comprehensive detection.
Shannon entropy measures the randomness or unpredictability of data on a scale from 0 (completely predictable) to 8 (maximum randomness). It's crucial for malware analysis because malicious software often uses packers, encryptors, or obfuscators that produce high-entropy output to evade antivirus detection, making entropy a quick first indicator of potential threats.
Malware authors use packers and encryption to evade signature-based detection by antivirus software. These techniques transform the original malicious code into encrypted or compressed data that appears random, hiding recognizable patterns and code signatures. Once executed, the malware unpacks itself in memory to run its payload.
Files with entropy above 7.0 are likely packed or encrypted, while values above 7.2 almost certainly indicate encryption or compression. Normal executable code typically ranges from 4.0 to 6.5, and values between 6.8 and 7.0 are considered suspicious and warrant further investigation.
Simply drag and drop any binary file (up to 50MB) into the upload area or click to browse for a file. The tool immediately calculates the overall Shannon entropy, analyzes 1KB sections across the file, shows byte distribution patterns, and provides an automated assessment with specific warnings and recommendations for further analysis.
No, high entropy doesn't automatically mean a file is malicious. Legitimate compressed files (ZIP, 7z), encrypted documents, multimedia files, and legally protected software often have high entropy. Always combine entropy analysis with other indicators like file metadata, digital signatures, source reputation, and behavioral analysis.
File entropy is the overall randomness score for the entire file, while section entropy breaks the file into chunks (1KB sections) and calculates entropy for each. Section analysis helps identify localized high-entropy regions that might indicate embedded encrypted payloads or packed code segments hidden within otherwise normal files.
No, entropy analysis only detects packed, encrypted, or obfuscated malware. It won't identify unpacked malware with normal entropy levels, polymorphic malware that mimics legitimate files, or scripts and macros. Entropy is best used as one component of a comprehensive malware detection strategy alongside signature-based scanning, heuristic analysis, and sandboxing.