This course applies machine learning to real cybersecurity problems, moving from foundational AI\/ML concepts through supervised, unsupervised, and deep learning models. Coursework covers spam and malware classification, intrusion detection with ensemble methods, anomaly detection on network traffic, and time-series analysis using LSTM networks. Hands-on projects use real-world datasets in Python, with emphasis on model evaluation, performance tuning, and the ethical and privacy considerations that govern AI-driven security tooling.<\/p>\n\n\n
This coursework demonstrates the ability to take a security problem, select an appropriate model, train and evaluate it against real data, and interpret the results in operational terms. It bridges the gap between security operations and data science: understanding not just which model to apply, but what its false-positive rate costs a SOC team, why a black-box model creates accountability risk, and where automated detection should defer to human review.<\/p>\n\n\n\n
\ud83d\udcd0 An analysis of how linear algebra and probability distributions operate inside four machine learning models used in security contexts: Naive Bayes, logistic and softmax regression, neural networks, and Gaussian anomaly detection. Examines tensor representation of feature data, matrix operations underlying model computation, and how Bernoulli and Gaussian distributions shape classification behavior and detection thresholds.<\/p>\n\n\nMod_1_Assignment_Fall2026_CYSE-420_fd