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 course section begins with linear regressions and ends with an introduction to Convolutional Neural Networks.
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, most of the material focuses on classification problems.
Building on the Bayes approach used in section 2, this course section introduces deep learning neural networks and fully connected dense networks through the development of a far more accurate phishing detection network. Following this, we'll explore visualization and measurement of neural network training performance, in addition to discussing overfitting and overtraining and how to identify (and avoid!) them.
The next portion of this section turns to categorical problems. Students will build a real-time network protocol classification system and, more importantly, implement anomaly detection in this classification system, a task typically reserved for unsupervised approaches.
The final portion of this course section will introduce Convolutional Neural networks. Further exploration of these continues in section 5.
- Polyfit Regressions
- Hello, World!, Ham vs. Spam
- Identifying Protocols
- Protocol Anomaly Detection
- Regression and Fitting
- Loss and Error Functions
- Vectors, Matrices, and Tensors
- Fundamentals of the Perceptron
- Dense Networks
- Convolutional Neural Networks