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Converted the core to ES modules

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n1474335 2018-03-26 23:14:23 +01:00
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src/core/operations/Entropy.js
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@ -1,195 +0,0 @@
import Utils from "../Utils.js";
/**
* Entropy operations.
*
* @author n1474335 [n1474335@gmail.com]
* @copyright Crown Copyright 2016
* @license Apache-2.0
*
* @namespace
*/
const Entropy = {
/**
* @constant
* @default
*/
CHUNK_SIZE: 1000,
/**
* Entropy operation.
*
* @param {byteArray} input
* @param {Object[]} args
* @returns {html}
*/
runEntropy: function(input, args) {
let chunkSize = args[0],
output = "",
entropy = Entropy._calcEntropy(input);
output += "Shannon entropy: " + entropy + "\n" +
"<br><canvas id='chart-area'></canvas><br>\n" +
"- 0 represents no randomness (i.e. all the bytes in the data have the same value) whereas 8, the maximum, represents a completely random string.\n" +
"- Standard English text usually falls somewhere between 3.5 and 5.\n" +
"- Properly encrypted or compressed data of a reasonable length should have an entropy of over 7.5.\n\n" +
"The following results show the entropy of chunks of the input data. Chunks with particularly high entropy could suggest encrypted or compressed sections.\n\n" +
"<br><script>\
var canvas = document.getElementById('chart-area'),\
parentRect = canvas.parentNode.getBoundingClientRect(),\
entropy = " + entropy + ",\
height = parentRect.height * 0.25;\
\
canvas.width = parentRect.width * 0.95;\
canvas.height = height > 150 ? 150 : height;\
\
CanvasComponents.drawScaleBar(canvas, entropy, 8, [\
{\
label: 'English text',\
min: 3.5,\
max: 5\
},{\
label: 'Encrypted/compressed',\
min: 7.5,\
max: 8\
}\
]);\
</script>";
let chunkEntropy = 0;
if (chunkSize !== 0) {
for (let i = 0; i < input.length; i += chunkSize) {
chunkEntropy = Entropy._calcEntropy(input.slice(i, i+chunkSize));
output += "Bytes " + i + " to " + (i+chunkSize) + ": " + chunkEntropy + "\n";
}
} else {
output += "Chunk size cannot be 0.";
}
return output;
},
/**
* @constant
* @default
*/
FREQ_ZEROS: false,
/**
* Frequency distribution operation.
*
* @param {ArrayBuffer} input
* @param {Object[]} args
* @returns {html}
*/
runFreqDistrib: function (input, args) {
const data = new Uint8Array(input);
if (!data.length) return "No data";
let distrib = new Array(256).fill(0),
percentages = new Array(256),
len = data.length,
showZeroes = args[0],
i;
// Count bytes
for (i = 0; i < len; i++) {
distrib[data[i]]++;
}
// Calculate percentages
let repr = 0;
for (i = 0; i < 256; i++) {
if (distrib[i] > 0) repr++;
percentages[i] = distrib[i] / len * 100;
}
// Print
let output = "<canvas id='chart-area'></canvas><br>" +
"Total data length: " + len +
"\nNumber of bytes represented: " + repr +
"\nNumber of bytes not represented: " + (256-repr) +
"\n\nByte Percentage\n" +
"<script>\
var canvas = document.getElementById('chart-area'),\
parentRect = canvas.parentNode.getBoundingClientRect(),\
scores = " + JSON.stringify(percentages) + ";\
\
canvas.width = parentRect.width * 0.95;\
canvas.height = parentRect.height * 0.9;\
\
CanvasComponents.drawBarChart(canvas, scores, 'Byte', 'Frequency %', 16, 6);\
</script>";
for (i = 0; i < 256; i++) {
if (distrib[i] || showZeroes) {
output += " " + Utils.hex(i, 2) + " (" +
(percentages[i].toFixed(2).replace(".00", "") + "%)").padEnd(8, " ") +
Array(Math.ceil(percentages[i])+1).join("|") + "\n";
}
}
return output;
},
/**
* Chi Square operation.
*
* @param {ArrayBuffer} data
* @param {Object[]} args
* @returns {number}
*/
runChiSq: function(input, args) {
const data = new Uint8Array(input);
let distArray = new Array(256).fill(0),
total = 0;
for (let i = 0; i < data.length; i++) {
distArray[data[i]]++;
}
for (let i = 0; i < distArray.length; i++) {
if (distArray[i] > 0) {
total += Math.pow(distArray[i] - data.length / 256, 2) / (data.length / 256);
}
}
return total;
},
/**
* Calculates the Shannon entropy for a given chunk of data.
*
* @private
* @param {byteArray} data
* @returns {number}
*/
_calcEntropy: function(data) {
let prob = [],
uniques = data.unique(),
str = Utils.byteArrayToChars(data),
i;
for (i = 0; i < uniques.length; i++) {
prob.push(str.count(Utils.chr(uniques[i])) / data.length);
}
let entropy = 0,
p;
for (i = 0; i < prob.length; i++) {
p = prob[i];
entropy += p * Math.log(p) / Math.log(2);
}
return -entropy;
},
};
export default Entropy;