Courses

Machine Learning for Cyber Security

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

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

Textbook

Getting Started with Deep Learning: Programming and Methodologies using Python
By Ricardo Calix

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

Code

GitHub
 

Videos

YouTube
 

Related Papers

Lab Environment

Environment:

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

Mon Tue Wed Thu Fri
8:30 AM

Lec: ML for Cyber Intro (slides) (video)
8:30 AM

Lec: Performance Metrics (slides) (video)
8:30 AM

Lab: Tensorflow Linear Regression (recap...)(code)
8:30 AM

Lec: Neural Nets and Deep Neural Nets (slides) (video)
8:30 AM

Lec/Lab: Unsupervised Learning sklearn
9:00 AM

Lec: ML for Cyber Intro (slides) (video)
9:00 AM

Lec: KNN (slides) (video)
9:00 AM

Lec: Deep Learning intro (slides) (video)
9:00 AM

Lec: Neural Nets and Deep Neural Nets (slides) (video)
9:00 AM

Lec/Lab: KMeans (code) and SVD sklearn
10:00 AM

break
10:00 AM

break
10:00 AM

break
10:00 AM

break
10:00 AM

break
10:15 AM

Lec: Datasets and Features (slides) (video)
10:15 AM


Naive bayes (slides) (video)
10:15 AM

Lec: Linear Regression (slides) (video)
10:15 AM

Lec: Neural Nets and Deep Neural Nets (slides) (video)
10:15 AM

Lec: Deep Reinforcement Learning (slides) (video)
11:00 AM

Lab: WEKA (Iris Lab Doc) (slides) (video)
11:00 AM

Lec, Lab: Sklearn (slides) (video)
11:00 AM

Lec: Linear Regression to Logistic Regression (slides) (video)
11:00 AM

Lab: KDD GPU Tensorflow (KDD GPU Lab Doc) (KDD Lab Doc) (video)
11:00 AM

Lab: Deep Reinforcement Learning (code)
11:45 AM

Short Quiz
11:45 AM

Short Quiz
11:45 AM

Short Quiz (link)
11:45 AM

Short Quiz
11:45 AM

Short Quiz
12:00 PM

lunch
12:00 PM

lunch
12:00 PM

lunch
12:00 PM

lunch
12:00 PM

lunch
1:00 PM

lunch
1:00 PM

lunch
1:00 PM

lunch
1:00 PM

lunch
1:00 PM

1:30 PM

Lec: IOT Problem Intro (slides) (video)
1:30 PM

Lab: Malware WEKA (Lab Doc) (video)
1:30 PM

Lab: Logistic Regression Tensorflow (code)
1:30 PM

Lab: IOT Tensorflow (Lab Doc)
1:30 PM

Wrap up and Exit Survey
2:00 PM

Lab: IOT Feature extraction (Lab Doc) (video)
Lab: IOT WEKA (Lab Doc) (video)
2:00 PM

Lab: IOT Device detection Sklearn (Lab Doc)
Lab: Malware sklearn (Lab Doc) (video)
2:00 PM

Lec: Logistic Regression (slides) (video)
2:00 PM

Lab: Malware Tensorflow (Lab Doc) (video)
2:00 PM

3:00 PM

break
3:00 PM

break
3:00 PM

break
3:00 PM

break
3:00 PM

3:15 PM

Lec: Malware Feature extraction intro (slides) (video)
3:15 PM

Lec: Data for Tensorflow (slides) (video 1) (video 2)
3:15 PM

Lec: Logistic Regression (slides) (video)
3:15 PM

Lab: Solve Phishing Lab (Lab Doc)
3:15 PM

4:00 PM

Lab: Malware feature extraction (Lab Doc) (slides) (video)
4:00 PM

Lab: Tensorflow Linear Regression (run and read the code)(code)
4:00 PM

Lab: KDD small (KDD-small Lab Doc)
4:00 PM

Challenge Lab: Credit card fraud (solve this with all your tools and try to achieve best performance metrics) (dataset) (info)
4:00 PM

4:30 PM

Problem Quiz (Link)
4:30 PM

Problem Quiz
4:30 PM

Problem Quiz
4:30 PM

Problem Quiz
4:30 PM

5:00 PM

5:00 PM

5:00 PM

5:00 PM

5:00 PM