Semester	Spring Semester, 2021
Department	Junior Class A, Department of Management Information Systems Junior Class B, Department of Management Information Systems Senior Class A, Department of Management Information Systems Senior Class B, Department of Management Information Systems
Course Name	Data Science for Cybersecurity
Instructor	HSIAO SHUN-WEN
Credit	3.0
Course Type	Elective
Prerequisite	Programming Language I,Programming Language II

Course Objective

Course Description

Course Schedule

週次 Week	課程主題 Topic	課程內容與指定閱讀 Content and Reading Assignment	教學活動與作業 Teaching Activities and Homework	學習投入時間 Student workload expectation
週次 Week	課程主題 Topic	課程內容與指定閱讀 Content and Reading Assignment	教學活動與作業 Teaching Activities and Homework	課堂講授 In-class Hours	課程前後 Outside-of-class Hours
1	Introduction to Cybersecurity	Security Management, Cyber Attack, Data Analysis Environment	Lecture.	3	6
2	Supervised Learning: classification I	Lecture: Table-Based Data and Data Analysis Process, Visualized Machine Learning Tool	Lecture. Homework: simple classification	3	6
3	Supervised Learning: classification II	Lecture: Supervised Learning Algorithms, Tree-based Classification	Lecture. Homework: tree-based classification	3	6
4	Unsupervised Learning: clustering	Lecture: Unsupervised Learning Algorithms, Problematic Data	Lecture. Homework: clustering email, missing values	3	6
5	Static Analysis	Lecture: Static Analysis, Digital Signature	Lecture. Class Demonstration. Homework: implement a PE parser	3	6
6	Dynamic Analysis	Lecture: System Call and API Call, Cuckoo	Lecture. Homework: implement API call sequence classifier	3	6
7	Trace and Log	Lecture: Network: Packet Capture, Netflow, Event Log	Lecture. Homework: packet filter	3	6
8	Reserved	Lecture	Lecture.	3	6
9	Midterm	Midterm	Midterm	3	6
10	Deep Learning Basics	Lecture: The concept of Neural Network.	Lecture.	3	6
11	Latent Space	Lecture: Activation Function Visualization, Auto Encoder	Lecture. Homework: MNIST	3	6
12	Latent Space II	Lecture: K-means and Self-Organizing Map, Word Embedding	Lecture. Homework: SOM	3	6
13	Language Model	Lecture: Bert	Lecture. Homework: downstream detection	3	6
14	Text-based Analysis with Orange	Lecture: The concept of data visualization.	Lecture. Homework.	3	6
15	Intrusion Detection	Intrusion Detection	Lab	3	6
16	Anomaly Detection	Anomaly Detection	Lab	3	6
17	Project Presentation	Project Presentation	Project Presentation	3	6
18	Final	Final	Final	3	6

Teaching Methods

Teaching Assistant

TBA

Requirement/Grading

Homework (30%): programming exercises and essays. You MUST see the ACADEMIC INTEGRITY section before taking this class.

Class Participation (10%): attendance, discussion. Students are expected to attend classes and participate in class discussions. It’s important that you attend and participate in class; our class meets only once a week, so missing one class represents a substantial portion of the semester. If there are special circumstances requiring you to be out of class, please email me/TA BEFORE class. You should come to class prepared and on time. You get ONE freebie absence. Your second absence is excusable in a dire emergency (e.g., illness, family emergency, flood, volcano, locusts, etc). A third absence can mean you fail the class.

Project (20%): student needs to write an analysis program on a security-related data set to demonstrate their understanding of security issues and data analysis skill. A proposal, a report, a presentation, and uploaded GitHub codes are required.

Midterm and Final (40%)

The Problem Solving Through Inquiry and Data Analysis rubric can be found here. You MUST read it carefully before submitting your first homework. It allows you to know exactly the way in which you will be assessed, it is helpful in facilitating academic integrity.

Academic Integrity

Plagiarism is a serious breach of academic trust. In academic work, our words, ideas and programs are the value of our work, so turning in someone else’s work as if it were your own is a form of theft. When you use someone else’s words, ideas, or programs without crediting the source or authorship of those words, ideas, and program, you are plagiarizing. So here’s the bottom line: original work only, credit to ideas, writing, words, or programs from someone other than you. Plagiarized work will automatically receive a “0” or “F” for the assignment.

Since cheating usually arises out of desperation and everyone has the occasional problem and finishes their work late, this class accepts late homework submission, but with a 15% per day penalty. We encourage you to complete your homework rather than drop it. Any oral discussion with classmates, TA and lecturer is welcomed, but you MUST NOT share any of your code in any form.

Textbook & Reference

Supervised Learning: classification Network Security Through Data Analysis, Michael Collins, OREILLY, 2014.

Data-Driven Security: Analysis, Visualization and Dashboards, Jay Jacobs and Bob Rudis, Wiley, 2014.

Machine Learning for Cyber Security

Data Science for Cyber-Security

Awesome Machine Learning for Cyber Security

Python Data Science Handbook

Malware Data Science: Attack Detection and Attribution, Joshua Saxe and Hillary Sanders, No Starch Press, Nov. 2018.

Python for Data Analysis, Wes McKinney, O'Reilly Media, October 2012.

https://machinelearningmastery.com

Urls about Course

https://sites.google.com/view/mikehsiao/teaching/data-science-for-cybersecurity-2021

Attachment