The entire focus of this section is on the theory, development, and use of supervised learning approaches in the field of information security. Building on the mathematics and statistics covered in section 2, this section begins with linear regressions and ends with the application of deep learning neural networks to multi-class classification problems involving real-time network data.
The material is focused on using supervised machine learning and mathematics to create predictive models. The initial discussion and exercises center around forecasting and trends analysis for anomaly detection. Following this, the majority of the material focuses on classification problems.
Building on the Bayes approach used in section 2, this section introduces deep learning neural networks and fully connected dense networks through the development of a far more accurate phishing detection network. Following this, the course explores visualization and measurement of neural network training performance, in addition to discussing overfitting, overtraining, and how to identify (and avoid!) them.
The next portion of this section turns to categorical problems, during which students will build a real-time network protocol classification system. More importantly, students will implement anomaly detection in this classification system, a task typically reserved for unsupervised approaches.
- Polyfit Regressions
- Hello, World! Sentiment Analysis
- Ham vs. Spam via Deep Learning
- Identifying Protocols
- Protocol Anomaly Detection
- Regression and fitting
- Loss and Error functions
- Vectors, Matrices, and Tensors
- Fundamentals of the Perceptron
- Dense Networks