Semester	Spring Semester, 2020
Department	MA Program of Management Information Systems, First Year MA Program of Management Information Systems, Second Year
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	Security Management & Data Analysis Environment	Lecture: introduction to security and the relationship to security management.	Lecture. Lab	3	6
2	Static Malware Analysis	Lecture: static analysis concept and dataset	Lecture. Homework: implement a static analysis system	3	6
3	Dynamic Malware Analysis	Lecture: dynamic analysis concept and dataset	Lecture. Homework: implement a dynamic analysis system	3	6
4	Network Trace and System Log	Lecture: NetFlow concept and dataset	Lecture. Homework: capturing network packets	3	6
5	Data Analysis Algorithm I: supervised learning	Lecture: Data analysis algorithms, including distance, similarity, classification, clustering for security application	Lecture. Class Demonstration. Homework: implement a supervised learning method	3	6
6	Data Analysis Algorithm II: unsupervised learning	Lecture: Data analysis algorithms, including distance, similarity, classification, clustering for security application	Lecture. Homework: implement distance function and clustering methods	3	6
7	Intrusion Detection System	Lecture: The concept of detection, the detection subjects, profiling, misuse detection, anomaly detection.	Lecture. Homework: the pros and cons of detection solutions	3	6
8	Anomaly Detection on NetFlow System	Lecture: Anomaly detection on numerical data, and introduction to DoS, entropy-based detection.	Lecture. Homework: the problem of anomaly detection approaches.	3	6
9	Midterm	Midterm	Midterm	3	6
10	Neural Network	Lecture: The concept of Neural Network.	Lecture. Homework: building NN	3	6
11	Neural Network II	Lecture: Keras.	Lecture. Homework: detect malware by NN	3	6
12	Spam Mail Filtering System	Lecture: The concept of text mining, machine learning and spam mail filtering.	Lecture. Homework: what else for filtering?	3	6
13	Sequence Analysis System	Lecture: The concept of text mining, machine learning and API calls.	Lecture. Homework: set or sequence?	3	6
14	Visualization	Lecture: The concept of data visualization.	Lecture. Homework.	3	6
15	Reserved	Reserved	Reserved	3	6
16	Project Presentation	Project Presentation	Project Presentation	3	6
17	Project Presentation	Project Presentation	Project Presentation	3	6
18	Final	Final	Final	3	6

Teaching Methods

Teaching Assistant

TBA

Requirement/Grading

Grading Policy

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 an 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 are welcomed, but you MUST NOT share any of your code in any form.

Textbook & Reference

•   Network Security Through Data Analysis, Michael Collins, OREILLY, 2014.

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

•   https://github.com/wtsxDev/Machine-Learning-for-Cyber-Security

•   Data Science for Cyber-Security, https://www.worldscientific.com/worldscibooks/10.1142/q0167#t=toc

•   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.

•   簡禎富, 許嘉裕, “大數據分析與資料挖礦”, 2/e, 前程文化, 2019, 02.

•   https://www.udemy.com/course/cybersecurity-data-science/

Urls about Course

https://sites.google.com/view/mikehsiao/teaching/data-science-for-cybersecurity-2020

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