TBA

• 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.

• 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