SemesterSpring Semester, 2020
DepartmentJunior 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 NameData Science for Cybersecurity
InstructorHSIAO SHUN-WEN
Credit3.0
Course TypeElective
PrerequisiteProgramming 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



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%)

- Class Participation (10%): attendance, discussion. Students are expected to attend classes and participate in class discussions.

- 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 report, a presentation, GitHub code upload are required.

- Midterm, Final (40%)

 



The Problem Solving Through Inquiry and Data Analysis rubric used in this class for grading can be found here. You MUST read it carefully before submitting your first homework.

https://drive.google.com/open?id=1LY6eQgwXiIQ_I9v2mxAL7xTLKsb7t8vZ


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-analysis-for-cybersecurity-2020
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