Courses

Machine Learning for Cyber Security

This course will cover the fundamental concepts related to machine learning for cyber security. Topics include:

• Deep Learning and ML
• Features and Feature extraction
• KNN, Linear Regression, Logistic Regression, Neural Nets, and Deep Neural Nets
• Performance Metrics
• ML applications: Malware, IOT detection, intrusion detection, Phishing, etc.
• Unsupervised Machine Learning

Prerequisites: Programming skills up to data structures and knowledge of statistics will be useful. No prior experience with machine learning is required.

Time & Place

8:30am-5:00pm
Mon, Tue, Wed, Thu, Fri (End 2:00 PM)
143 Powers

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Textbooks to get started with cyber security

Cyber books

Keep updated of AI developments at the AI hub

The AI hub

Instructor

Ricardo A. Calix, Ph.D.
Purdue University Northwest
rcalix@pnw.edu

Office Hours

Tuesday and Thursday (2-4 PM)
My office is at 241 Anderson. The course will take place in Powers 143

About Purdue University Northwest

• PNW Campus Map
• Powers Building map
• Parking: You can park on any open parking lot on campus. The 169th street parking is the closest to the Powers building. The faculty gated parking will also be open for a few days during the week of July 15-19.

Code

GitHub
 

Videos

YouTube
 

Related Papers

• On the Anatomy of the Dynamic Behavior of Polymorphic Viruses
• Feature Ranking and Support Vector Machines Classification Analysis of the NSL-KDD Intrusion Detection Corpus
• Text message corpus: Applying natural language processing to mobile device forensics
• Phishing Websites Features

Lab Environment

Environment:

• we will provide a flash drive with a VM (download) for you to use. The VM password is: P@ssw0rd
• we also have windows/linux computers and access to cloud services

AWS

• You will need the .pem file provided in your flash drive

Lab Materials and data files

1. All Lab materials and data files on GitHub

Recommendations on sources and products

1. AI
2. Cyber Security

Course Materials

1. All Labs on CLARK

Tools

Example problems will be provided as required. 

We will use the following software:

1. Sklearn
2. Python
3. Tensorflow
4. Weka

Calendar Summer 2019 - Intensive 1 week program