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This document discusses building a machine learning model to detect potential cyberattack types using network connection data from the KDD Cup 1999 dataset. The objective is to classify connection data into normal and attack types. The document compares various data preprocessing techniques like feature selection, encoding, and scaling that will be used to preprocess the data before training a deep learning model for classification. Feature selection methods include chi-squared test, random forest, and extra trees classifiers. Encoding methods include one-hot, binary, frequency, and label encoding. Scaling methods include min-max, standardization, binarizing, and normalizing. An autoencoder will further process the preprocessed data to extract optimal features for classification using a deep neural network.