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Needs tiding but it does function
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n1073645
parent
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commit
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1 changed files with 339 additions and 61 deletions
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@ -6,7 +6,7 @@
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import Operation from "../Operation.mjs";
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import OperationError from "../errors/OperationError.mjs";
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//import Entropy from "./Entropy.mjs";
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import Utils from "../Utils.mjs";
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/**
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* Extract Entropies operation
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@ -24,7 +24,8 @@ class ExtractEntropies extends Operation {
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this.description = "";
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this.infoURL = "";
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this.inputType = "ArrayBuffer";
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this.outputType = "List<File>";
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this.outputType = "JSON";
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this.presentType = "html";
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this.args = [
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{
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name: "Entropies",
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@ -43,7 +44,7 @@ class ExtractEntropies extends Operation {
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off: [2, 3]
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},
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{
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name: "Ascii Range",
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name: "English Text",
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off: [2, 3]
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},
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{
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@ -54,8 +55,8 @@ class ExtractEntropies extends Operation {
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},
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{
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name: "Block Size",
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type: "number",
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value: "8"
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type: "string",
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value: "10"
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},
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{
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name: "Lower Entropy Bound",
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@ -66,6 +67,16 @@ class ExtractEntropies extends Operation {
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name: "Upper Entropy Bound",
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type: "number",
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value: 0
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},
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{
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name: "Algorithm To Use",
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type: "option",
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value: ["Absolute Mean Deviation", "Standard Deviation"]
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},
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{
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name: "Flip Output",
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type: "boolean",
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value: false
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}
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];
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}
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@ -106,7 +117,7 @@ class ExtractEntropies extends Operation {
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}
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/**
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* Calculates the scanning entropy of the input
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* Calculates the scanning entropy of the input.
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*
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* @param {Uint8Array} inputBytes
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* @returns {Object}
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@ -123,28 +134,59 @@ class ExtractEntropies extends Operation {
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return { entropyData, binWidth };
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}
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/**
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* Calculates the average of a list of entropies.
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*
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* @param {Array} entropies
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* @returns {number}
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*/
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generateAverage(entropies) {
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return entropies.reduce((previous, current) => current += previous) / entropies.length;
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}
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/**
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* Caculates the mean of the data above the original mean.
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*
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* @param {Array} entropies
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* @param {number} meanEntropy
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* @returns {number}
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*/
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getAboveMean(entropies, meanEntropy) {
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const result = [];
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let total = 0, count = 0;
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entropies.forEach((element) => {
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if (element > meanEntropy)
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result.push(element - meanEntropy);
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if (element > meanEntropy) {
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total += (element - meanEntropy);
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count++;
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}
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});
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return this.generateAverage(result);
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return total/count;
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}
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/**
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* Caculates the mean of the data below the original mean.
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*
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* @param {Array} entropies
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* @param {number} meanEntropy
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* @returns {number}
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*/
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getBelowMean(entropies, meanEntropy) {
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const result = [];
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let total = 0, count = 0;
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entropies.forEach((element) => {
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if (element < meanEntropy)
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result.push(meanEntropy - element);
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if (element < meanEntropy) {
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total += (meanEntropy - element);
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count++;
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}
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});
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return this.generateAverage(result);
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return total/count;
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}
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/**
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* Retrieves all the blocks with entropy higher than the high mean.
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*
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* @param {Array} entropies
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* @param {number} highEntropy
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* @returns {Array}
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*/
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getAllAbove(entropies, highEntropy) {
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const result = [];
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entropies.forEach((element, index) => {
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@ -154,6 +196,13 @@ class ExtractEntropies extends Operation {
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return result;
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}
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/**
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* Retrieves all the blocks with entropy lower than the low mean.
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*
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* @param {Array} entropies
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* @param {number} highEntropy
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* @returns {Array}
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*/
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getAllBelow(entropies, lowEntropy) {
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const result = [];
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entropies.forEach((element, index) => {
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@ -163,54 +212,283 @@ class ExtractEntropies extends Operation {
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return result;
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}
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/**
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* Calculates the standard deviation of all of the entropies.
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*
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* @param {Array} entropies
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* @param {number} meanEntropy
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* @returns {number}
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*/
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calculateStandardDeviation(entropies, meanEntropy) {
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let total = 0;
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entropies.forEach((element) => {
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total += Math.pow((element-meanEntropy), 2);
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});
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return Math.sqrt(total/(entropies.length-1));
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}
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/**
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* Calculates the standard deviation of all of the entropies below the standard deviation of the whole data.
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*
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* @param {Array} entropies
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* @param {number} meanEntropy
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* @returns {number}
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*/
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getBelowStandardDeviation(entropies, meanEntropy) {
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const result = [];
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entropies.forEach((element) => {
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if (element < meanEntropy) {
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result.push(element);
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}
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});
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return this.calculateStandardDeviation(result, meanEntropy);
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}
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/**
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* Calculates the standard deviation of all of the entropies above the standard deviation of the whole data.
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*
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* @param {Array} entropies
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* @param {number} meanEntropy
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* @returns {number}
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*/
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getAboveStandardDeviation(entropies, meanEntropy) {
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const result = [];
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entropies.forEach((element) => {
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if (element > meanEntropy) {
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result.push(element);
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}
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});
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return this.calculateStandardDeviation(result, meanEntropy);
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}
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/**
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* Determines which algorithm to use for calculating the deviation.
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*
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* @param {string} algorithm
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* @param {Array} entropies
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* @param {number} meanEntropy
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* @returns {number}
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*/
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algorithmTypeLow(algorithm, entropies, meanEntropy) {
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switch (algorithm) {
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case "Absolute Mean Deviation":
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return this.getBelowMean(entropies.entropyData, meanEntropy);
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case "Standard Deviation":
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return this.getBelowStandardDeviation(entropies.entropyData, meanEntropy);
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}
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}
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/**
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* Determines which algorithm to use for calculating the deviation.
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*
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* @param {string} algorithm
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* @param {Array} entropies
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* @param {number} meanEntropy
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* @returns {number}
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*/
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algorithmTypeHigh(algorithm, entropies, meanEntropy) {
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switch (algorithm) {
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case "Absolute Mean Deviation":
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return this.getAboveMean(entropies.entropyData, meanEntropy);
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case "Standard Deviation":
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return this.getAboveStandardDeviation(entropies.entropyData, meanEntropy);
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}
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}
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/**
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* Retrieves all of the blocks with entropy between the lower bound and upper bound.
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*
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* @param {Array} entropies
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* @param {number} lbound
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* @param {number} ubound
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* @param {ArrayBuffer} input
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* @param {boolean} flipGroupings
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* @param {number} binWidth
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* @returns {string}
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*/
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getRange(entropies, lbound, ubound, input, flipGroupings, binWidth) {
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const result = [];
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entropies.forEach((element, index) => {
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if (element > lbound && element < ubound)
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result.push([element, index]);
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});
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return this.generateOutput(result, input, flipGroupings, binWidth);
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}
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/**
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* Determines the data ranges for blocks with similar entropies.
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*
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* @param {Array} data
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* @returns {Array}
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*/
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groupings(data) {
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const result = [data[0][1]];
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for (let i = 0; i < data.length - 1; i++) {
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if (Math.abs(data[i][1] - data[i+1][1]) > 3) // I think this needs to be scaled on the bock size rather than a hardcoded value.
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result.push(data[i][1], data[i+1][1]);
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}
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result.push(data[data.length-1][1]);
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return result;
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}
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/**
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* Flips the groups to cover the remaining data.
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*
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* @param {Array} data
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* @returns {Array}
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*/
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flipGroupings(data) {
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const result = [];
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if (data[0]) {
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result.push(0, data[0] - 1);
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}
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for (let i = 1; i < data.length-1; i+=2) {
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result.push(data[i]+1, data[i+1]);
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}
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return result;
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}
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/**
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* Frames the data into a html format.
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*
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* @param {Array} ranges
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* @param {ArrayBuffer} input
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* @param {boolean} flipOutput
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* @param {number} binWidth
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* @returns {string}
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*/
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generateOutput(ranges, input, flipOutput, binWidth) {
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let output = "";
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if (!(ranges.length))
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return output;
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ranges = this.groupings(ranges);
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if (flipOutput)
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ranges = this.flipGroupings(ranges);
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for (let i = 0; i < ranges.length; i+=2) {
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if (i === 0)
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output +=`
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<td>${binWidth}</td>
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`;
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else
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output += `
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<td></td>
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`;
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output += `<td>${(ranges[i]*binWidth).toString(16)}-${(((ranges[i+1]+1)*binWidth)-1).toString(16)}</td>
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<td>${Utils.escapeHtml(Utils.printable(Utils.truncate(Utils.arrayBufferToStr(input.slice(ranges[i]*binWidth, ((ranges[i+1]+1)*binWidth)-1)), 99)))}</td>
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</tr>`;
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}
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return output;
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}
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/**
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* Puts the remaining data into the table html format.
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*
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* @param {string} data
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* @returns {string}
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*/
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generateCompleteOutput(data) {
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if (!(data))
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return "Nothing of interest could be found in the input data.\nHave you tried changing the block size?";
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return `<table
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class='table table-hover table-sm table-bordered'
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style='table-layout: fixed;'>
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<tr>
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<th>Block Size</th>
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<th>Offset</th>
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<th>Result snippet</th>
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</tr>`+ data + "</table><script type='application/javascript'>$('[data-toggle=\"tooltip\"]').tooltip()</script>";
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}
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/**
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* Dispatches on the type of output we want.
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*
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* @param {Array} entropies
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* @param {string} input
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* @param {string} algorithm
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* @param {boolean} flipOutput
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* @param {number} binWidth
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* @param {ArrayBuffer} data
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* @returns {string}
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*/
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entropyCalculations(entropies, input, algorithm, flipOutput, binWidth, data) {
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let aboveScale = 0;
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let belowScale = 0;
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let highResults = [];
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let lowResults = [];
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const meanEntropy = this.generateAverage(entropies.entropyData);
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switch (input) {
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case "High":
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aboveScale = this.algorithmTypeHigh(algorithm, entropies, meanEntropy);
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highResults = this.getAllAbove(entropies.entropyData, meanEntropy+aboveScale);
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return this.generateOutput(highResults, data, flipOutput, binWidth);
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case "Low":
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belowScale = this.algorithmTypeLow(algorithm, entropies, meanEntropy);
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lowResults = this.getAllBelow(entropies.entropyData, meanEntropy-belowScale);
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return this.generateOutput(lowResults, data, flipOutput, binWidth);
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case "Low and High":
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aboveScale = this.algorithmTypeHigh(algorithm, entropies, meanEntropy);
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highResults = this.getAllAbove(entropies.entropyData, meanEntropy+aboveScale);
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belowScale = this.algorithmTypeLow(algorithm, entropies, meanEntropy);
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lowResults = this.getAllBelow(entropies.entropyData, meanEntropy-belowScale);
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return this.generateOutput(lowResults, data, flipOutput, binWidth) + this.generateOutput(highResults, data, flipOutput, binWidth);
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}
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}
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/**
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* @param {ArrayBuffer} input
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* @param {Object[]} args
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* @returns {List<File>}
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* @returns {JSON}
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*/
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run(input, args) {
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args[1] = parseInt(args[1]);
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main(input, args) {
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if (args[2] < 0)
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throw new OperationError("Cannot have a lower bound entropy lower than 0");
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if (args[3] > 8)
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throw new OperationError("Cannot have an upper bound entropy greater than 8");
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if (args[1] >= input.byteLength)
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throw new OperationError("Block size is larger than the input");
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if (args[1] < 0)
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throw new OperationError("Cannot have a negative block size");
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let highMean = 0;
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let lowMean = 0;
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let highResults = [];
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let lowResults = [];
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let result = [];
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const entropies = this.calculateScanningEntropy(new Uint8Array(input), args[1]);
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switch (args[0]) {
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case "Ascii Range":
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case "English Text":
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result = this.getRange(entropies.entropyData, 3.5, 5, input, args[5], args[1]);
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break;
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case "Enter Value Range":
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if (args[2] === args[3])
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throw new OperationError("Should not have the upper bound entropy and the lower bound entropy equal to one another");
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if (args[2] > args[3])
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throw new OperationError("Should not have lower bound entropy higher than the highere bound entropy");
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result = this.getRange(entropies.entropyData, args[2], args[3], input, args[5], args[1]);
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break;
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default:
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const entropies = this.calculateScanningEntropy(new Uint8Array(input), args[1]);
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const meanEntropy = this.generateAverage(entropies.entropyData);
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switch (args[0]) {
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case "High":
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highMean = this.getAboveMean(entropies.entropyData, meanEntropy);
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highResults = this.getAllAbove(entropies.entropyData, meanEntropy+highMean);
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break;
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case "Low":
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lowMean = this.getBelowMean(entropies.entropyData, meanEntropy);
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lowResults = this.getAllBelow(entropies.entropyData, meanEntropy-lowMean);
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console.log(lowResults);
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console.log("All entropies: ", entropies);
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break;
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case "Low and High":
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highMean = this.getAboveMean(entropies.entropyData, meanEntropy);
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highResults = this.getAllAbove(entropies.entropyData, meanEntropy+highMean);
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lowMean = this.getBelowMean(entropies.entropyData, meanEntropy);
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lowResults = this.getAllBelow(entropies.entropyData, meanEntropy-lowMean);
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break;
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result = this.entropyCalculations(entropies, args[0], args[4], args[5], args[1], input);
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}
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return result;
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}
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return " ";
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/**
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* @param {ArrayBuffer} input
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* @param {Object[]} args
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* @returns {JSON}
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*/
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run(input, args) {
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let result = "";
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if (args[1].indexOf("-") !== -1) {
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const temp = args[1].split("-");
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for (let i = parseInt(temp[0], 10); i <parseInt(temp[1], 10); i++) {
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args[1] = i;
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result += this.main(input, args);
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}
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} else {
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args[1] = parseInt(args[1], 10);
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result = this.main(input, args);
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}
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return this.generateCompleteOutput(result);
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}
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}
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