Julia 1.0 was released in 2018. It is a language created to have both the high-level simplicity as python, but low-level performance as C. In this tutorial we will do some cool coding with it… Julia is a comparably new language that aimed to have the performance of C and simplicity of Python. Having the ability to perform data analysis without much trouble while shipping the code with competitive performance, Julia is expected to be a powerful tool in businesses. But I think it also have some great potentials regarding to the current trends in . FinTech Cybersecurity Julia, Image from Medium In this article, I will explain why Julia can also be a great tool for the future of . Meanwhile, I will share how I wrote a Julia script on my computer to crack a cipher text encrypted with and . In the end, I will share some performance comparisons and the code for this project in the appendix. Cybersecurity Mac Caesar Code Shift Columnar Transposition Cryptography, from Caesar Code Shift to Biometric Authentication First let’s review some of the basics in cybersecurity. How we encrypt our messages and authenticate users. We will go over the old school Caesar code shift and columnar transposition, which are involved in the code example. And then, we will go over biometric authentication and why Julia can be a great tool for the future of cybersecurity. Caesar Code Shift & Columnar Transposition As human we communicate with each other, but sometimes we do not want everyone to know what we are communicating about. Since Ancient Rome have been sending secret military messages by shifting code letters in the message. For example “A” will be shifted to “B” and “Z” will be shifted to “A”. The word “HAL” will be shifted into “IBM”. The technique was later on named after him, known as the . Julius Caesar Caesar Cipher A Caesar cipher disk, image from Wikipedia There was a huge vulnerability to such code-shift based encryption. During the early time of the second world war, the mathematicians analyzed the frequency of occurrence of each alphabet in Germany. By comparing the frequency bar charts, they were able to figure out the shift. The story extends to how the Germans then invented the , and how managed to crack it with his giant computing machine. Enigma Alan Turing Enigma, image from BT.com There were many other techniques we used to encrypt our messages. Another one is the . Which involves putting the text into a table, then rearrange the columns and read the text back. columnar transposition A video demo of columnar transposition Other encryption methods such as , , and are widely used in sending information securely over the internet. RSA for example, is an taking advantage of the properties of . RSA ECC MD5 Asymmetric Encryption prime numbers Biometrical Authentication & Artificial Intelligence The first time our ancestors utilizes is probably way earlier than they started playing with code shifts. People distinguishes each other by looking at their different faces. Fingerprints for another example, is widely used as a credential for a person. The technology companies later on uses it to provide a simple and fast user experience in device authentication. For example, the on an iPhone. Biometrical Authentication Touch ID Touch ID illustrated, image from Cult of Mac As a sub-branch of , is widely used in various applications. It has been a known that there will be more artificial intelligence applied in the field of human computer interaction, which is widely encouraged in related . From my survey over hundreds of publications, Machine Learning, Computer Vision and Data Analysis have been proven to have great impact to the field of Biometric Authentication. Data Science and Artificial Intelligence will become a useful tool in on both the side and the side. Human Computer Interaction Biometric Analysis trend conferences Cybersecurity defender attacker Why Julia Matters? As a language designed for , and other popular technologies, Julia made research easier while not sacrificing performances by a lot. It is a language designed for us to ship our fancy data science algorithms without having to rewrite them on another language. Data Science Machine Learning Julia is a powerful tool for Data Science and Machine Learning As the increasing involvement of data science and artificial intelligence in cybersecurity, which also requires a good performance in its related applications, Julia definitely comes to be a handy fit. Want to lean about how it works? Let us go over an example right away! Cracking Caesar Code Shift and Columnar Transposition with Julia KUHPVIBQKVOSHWHXBPOFUXHRPVLLDDWVOSKWPREDDVVIDWQRBHBGLLBBPKQUNRVOHQEIRLWOKKRDD I was asked to decipher the above cipher text. The hint was it was ciphered with and with a key length shorter than 10. Deciphering those secret messages generally involves some kind of brute force approach, which can be computationally expensive. In order to reduce some of its complexity, collision with dictionaries is also necessary. Caesar Code Shift Columnar Transposition To make it fast, the first thing that came to my mind was . But those headers and memory management can be a pain to deal with. on the other hand, is supported by powerful libraries and minimalistic grammar, but sacrifices lots of speed. After some considerations, I end up doing it with . C Python Julia 1. Getting Started Now Let us get started! First, we will have to download Julia, a of the download can be found . You can also visit the official for other versions and info. Mac version here download page After the download, we can open the by double clicking on the application icon. command line console Opening Julia’s Command Line Console To run any scripts saved as local file, we can use the command inside the : include() command line console include("<PATH_TO_YOUR_JULIA_SCRIPT>/hello_world.jl") The name of the file has the extension . .jl Running Local Script in Julia Now in order to complete this project, we will also need to two packages: import The which we will need to generate the permutation for the columnar transposition Combinatorics The which we will need to show the deciphering progress. ProgressMeter We can do so with the following commands. Installing : Combinatorics #import Pkg; Pkg.add("Combinatorics") Installing : ProgressMeter import Pkg; Pkg.add("ProgressMeter") We also need a dictionary file which can be downloaded from . The dictionary contains commonly used English words which we can use in the future to collide with our cipher text. here 2. Importing Cipher Text and English Word Frequency We can in Julia just like in any other languages, and we do not need to specify the type. The function prints the string to a new line like many other languages such as Java. The strings are with a instead of a plus symbol. define a string println() concatenated comma cipherText = "KUHPVIBQKVOSHWHXBPOFUXHRPVLLDDWVOSKWPREDDVVIDWQRBHBGLLBBPKQUNRVOHQEIRLWOKKRDD"println("Begin to decrypt cipher text: ", cipherText) Each letters occurs at different frequency in English, where a list can be found . Different sources may have slightly different statistics. We can define a list of floating points in Julia with the following command. here println("Initializing frequency array for ENGLISH...")ENGLISH = [0.0749, 0.0129, 0.0354, 0.0362, 0.1400, 0.0218, 0.0174, 0.0422, 0.0665, 0.0027, 0.0047, 0.0357,0.0339, 0.0674, 0.0737, 0.0243, 0.0026, 0.0614, 0.0695, 0.0985, 0.0300, 0.0116, 0.0169, 0.0028,0.0164, 0.0004] Now we would like to count each of the letters, so we have to create a list of zeroes with the same size as there are alphabets in English. We can do so with the following commands. The length of the lists can be retrieved with . We can cast a character to integer with . The values can be incremented with a operator. A for loop in Julia looks like the following: length() Int() “+=” counts = zeros(length(ENGLISH))for letter in cipherTextcode = Int(letter) - Int('A')if code < 1code += 26endcounts[code] += 1end We do as many shifts as there are alphabets in the english language, totaling 26 shifts including no shifts. We can do so with the function. circshift() variances = zeros(length(ENGLISH))for shift in 0:length(ENGLISH) - 1println("Applying inverse caesar shift: ", shift)shiftedCounts = circshift(counts, -1 * shift)variance = sum(broadcast(abs, ((shiftedCounts / length(cipherText) - ENGLISH) / ENGLISH)))variances[shift + 1] = variance;endprintln("The variance for each shift is calculated to be: \n", variances)minshift = argmin(variances) Inside the for loop we calculate the difference between the counted frequencies and their frequencies in English words for each alphabet. Subtractions and divisions can be done directly at list level. The function maps the function to each elements inside the list similar to or . The function finds the total sum of the list. The variance for each letter is divided by their frequencies in English so the variance is not influenced by how frequent the letter appears. boardcast() abs() Scheme Java Script sum() variance = sum(broadcast(abs, ((counts / length(cipherText) - ENGLISH) / ENGLISH))) The list operations work similar to , while is used for multiplication. Features such as subtraction of an integer from an array is not yet supported. More information can be found in the . Python “.*” official documentation Array Operations in Julia Now if we run the code, we should get the following, the program figured that the . most plausible shift is 3 Finding the Best Shift Now we can print the shifted string with the following scripts. The function casts the integer code back to a character, the function appends the character to the list, and the function joins the into a . Char() append!() join() character list string shiftedTextList = []for letter in cipherTextcode = Int(letter) - minshiftif code < Int('A')code += 26endappend!(shiftedTextList, Char(code))endshiftedText = join(shiftedTextList)println("Deciphering minimal variance shifted text: ", shiftedText, " with columnar transposition...") Most Plausible Shift String Printed 3. Applying Columnar Transposition & Utilizing Dictionary Collision First, we have to reference the necessary packages we installed in the previous sections, we can do so with the following codes. These declarations can be put anywhere before they are used rather than having to be at the top of your code. using Combinatoricsusing ProgressMeter And then we can import our dictionary with the following code. filename = "<PATH_TO_YOUR_DICTIONARY_FILE>/google-10000-english.txt"println("Loading common words DICTIONARY from: ", filename)DICTIONARY = readlines(filename) This should print a snippet of the dictionary after we save and run the code. Loading Dictionary File In Julia Now we want to and . The following code will do the job. construct the column matrix iterate through each of the permutations for width in 2:3println("Applying columnar transposition with width: ", width)matrix = []height = Int(ceil(length(shiftedText) / width))total = width * heightfor index in 1:totalif index <= length(shiftedText)append!(matrix, shiftedText[index])elseappend!(matrix, "?")endendmatrix = reshape(matrix, (height, width))orders = collect(permutations(collect(1:width)))println(matrix)println(orders)end The function the number to the closest integer. We fill the blank spaces with . The function to the . We generate the orders with , which takes a list of elements as input and returns all the possible permutations. The input is generated with , where collect converts the range into a collection, in other words a list. ceil() rounds up “?” reshape() shapes the matrix specified dimensions permutations() collect(1:width) “1:width” Now after we should see the reconstructed columnar tables printed with the permutations. indicates a . save and reload Semicolons new row Columnar Tables and Permutations printed in Julia Before we go through the permutations, we should display a as the task is going to take some time. We do not want to sit and wonder whether the program is still running or not. With the packages we have imported, the following code will initialize the progress bar. progress bar before the loop n = length(orders)p = Progress(n, 1, "Computing all permutations:", 50, :black) And we put this in the end of the loop to the progress bar. For more info on how to use the , take a look at . update ProgressMeter here next!(p) The complete code for dictionary collision can be found below. Where it uses to capitalize the words in the dictionary, and to see if it appears in the rearranged text. Once the common words contributed to a significant amount of the rearranged text, it prints them out and keeps track of the permutation yielding the highest percentage of common words. uppercase() occursin() completed = 0n = length(orders)p = Progress(n, 1, "Computing all permutations:", 50, :black)bestPlainText = "?"bestPlainTextCommonPercentage = 0for permutation in ordersplainTextMatrix = []for row in 1:heightfor columnIndex in 1:widthchar = matrix[row, permutation[columnIndex]]if char != '?'append!(plainTextMatrix, char)endendendplainText = join(plainTextMatrix)wordCount = 0wordLengthSum = 0words = []for word in DICTIONARYif length(uppercase(word)) > 3 && occursin(uppercase(word), plainText)wordCount += 1;wordLengthSum += length(word)push!(words, uppercase(word))endendpercentageCommon = wordLengthSum / length(plainText) * 100if percentageCommon > 50println("\rColumnar transposition with the order of: ", permutation, " yielded a string containing ", wordCount, " common word(s) which makes it ", percentageCommon, "% common words, below is the string:\n", plainText, "\ncontaining the words:\n", words)if percentageCommon > bestPlainTextCommonPercentagebestPlainText = plainTextbestPlainTextCommonPercentage = percentageCommonendprintln("Currently, the best plain text is:\n", bestPlainText, " which is ", bestPlainTextCommonPercentage, "% common words...")endnext!(p)end This algorithm is not perfect, where the percentage has the potential to go over 100% if there are overlapping words. Making it perfect takes a bit too much work as you have to consider all the word permutations, so let us stick with the quick and dirty way for now. The percentage is also here just for the reference. Before we go for the test, we also have to of the table to 10, as we know that to be the maximum length of the key from our hints. increase the maximum width We change: for width in 2:3 into: for width in 2:10 And we should the following test code we had before: remove println(matrix)println(orders) Now we should be able to save and launch the code. Deciphering with Julia Scripts And after some time, we should see the plain text getting printed as it has the highest percentage of common words. The text is deciphered to be: BEHAPPYFORTHEMOMENTTHISMOMENTISYOURLIFEBYKHAYYAMOHANDALSOTHISCLASSISREALLYFUN Julia Script Successfully Decrypted the Text Congratulations!!! You have just learned how to write a cipher text cracker in Julia! Hope this to be a great start of your exploration in the language. In the end… Julia is a simple and powerful language in data analysis. You can read more about its performance from the links below: Basic Comparison of Python, Julia, Matlab, IDL and Java (2018 Edition) Basic Comparison of Python, Julia, R, Matlab and IDL A Speed Comparison Of C, Julia, Python, Numba, and Cython on LU Factorization You will see that the performance of goes way ahead over for larger input sizes, and even surpasses for matrix calculations. Matrix calculations are widely used in machine learning and data science, and the input size can generally become really large. Julia Python C I am a passionate coder who loves learning new things and share it with the community. If there are anything particular you would like to read please let me know. I mainly focus on , and right now, but there are lots of things I can write about. Artificial Intelligence Human Computer Interactions Robotics I can continue write on and maybe a little bit on . Many of my research works are confidential but once they are released, I will have lots to share. I can also re-create in , just like how I coded an in before their machine learning modules are well built. Hope you enjoyed learning these cool new stuffs as much as I did! Julia Data Science data science models Julia Artificial Neural Network Unity C# I have attached the complete code of this project to the appendix. Appendix (Complete Code of this Project): My code finished all the permutations after a total of 16 hours:
