ADFA 2017 Dataset: This dataset is used for anomaly detection, which contains real-world attack data. The changes involve processing and cleaning the data, then using it to train a machine learning model. Skipgram & TF-IDF: Skipgram (from Word2Vec) is used for extracting useful word representations from text data, and TF-IDF (Term Frequency-Inverse Document Frequency) is used for feature extraction from the processed text. These methods will help the model understand the context of network activity for detection purposes. Perceptron Classification: The Perceptron algorithm is chosen for its efficiency in handling linearly separable problems, and it is trained to classify network traffic as either normal or malicious. Hyperparameter Tuning: Grid search is applied to find the best hyperparameters for the Perceptron model, ensuring optimal performance. SageMaker Pipeline: The entire training process, including data preprocessing, model training, and evaluation, is automated and deployed on Amazon SageMaker pipelines. This allows for scaling, model retraining, and streamlined integration with other cloud services.