Machine Learning Techniques to Predict the Inputs in Symmetric Encryption Algorithm

Abstract

Applying machine learning algorithms for encryption problems is reasonable in today's research connecting with cryptography. Using an encryption standard such as DES can give insight into how machine learning can help in breaking the encryption standards. The inspiration for this chapter is to use machine learning to reverse engineer hash functions. Hash functions are supposed to be tough to reverse one-way functions. The hash function will be learned by machine learning algorithm with a probability of more than 50%, which means the can develop their guesstimate of the reverse. This is concluded by executing the DES symmetric encryption function to generate N numerous values of DES with a set key and the machine learning algorithm is trained on a neural network to identify the first bit of the input based on the value of the function's output. Testing has ended through a new table, which was created similarly but with different inputs. The SVM runs on the new table, and it compares to the other table, and a confusion matrix is used to measure the excellence of the guesstimates.