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Short Course - Applied Machine Learning for Security Informatics
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Short Course

Applied Machine Learning for Security Informatics

The use of statistical models in computer algorithms allows computers to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision—Amazon’s recommender system, Google’s driverless car and the most recent imaging systems for cancer diagnosis are all based on Machine Learning technology.

This course on Machine Learning will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include linear regression, logistic regression, deep neural networks, clustering, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, Fraud Detection, Phishing Detection, HTML Injections Classification, Clustering of Phishing Attackers, Malware Fingerprinting, Criminal Profiling, among others.

Instructor: Dr. Alejandro Correa Bahnsen


  • Python version 3.5;
  • Numpy, the core numerical extensions for linear algebra and multidimensional arrays;
  • Scipy, additional libraries for scientific programming;
  • Matplotlib, excellent plotting and graphing libraries;
  • IPython, with the additional libraries required for the notebook interface.
  • Pandas, Python version of R dataframe
  • scikit-learn, Machine learning library!

A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the Anaconda.

GIT!! Unfortunatelly out of the scope of this class, but please take a look at these tutorials


Session Notebook link Exercises
1 Introduction to Machine Learning
2 Introduction to Python 01 - Python & Numpy
3 Pandas Data Frame 03 - Baby names
4 Linear Regression 04 - Bikes Rent
5 Logistic Regression 05 - Intrusion Detection
6 Data Preparation and evaluation Intrusion Detection
7 Feature Selection Intrusion Detection
8 Decision Trees Fraud Detection
9 Ensemble Methods - Bagging Bagging
10 Ensemble Methods - Boosting
11 Support Vector Machines Phishing
12 Deep Learning
13 Model Deployment
14 Kaggle Competition
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