Course Objectives & Learning Outcomes

• Comprehend fundamental detection methodologies, including profiling targets, profiling techniques, misuse (signature-based) detection, and anomaly detection. 

• Master the core principles and workflows of both static and dynamic program analysis. 

• Analyze key machine learning and data analysis algorithms for security applications, encompassing distance/similarity metrics, classification, and clustering techniques. 

• Examine deep learning architectures and neural network algorithms applied to threat identification. 

• Explore the application of language models (LMs/LLMs) in analyzing security telemetry and unstructured threat intelligence. 

• Evaluate real-world security information systems through a data-driven lens, including intrusion detection systems (IDS), anomaly detection frameworks, spam filtering solutions, and sequence analysis pipelines.

Grading Policy

•  Homework (50%): programming exercises and essays. You MUST see the ACADEMIC INTEGRITY section before taking this class.

• Project (10%): 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 GitHub codes are required.

• Midterm (20%)

• Final (20%)

The Problem Solving Through Inquiry and Data Analysis rubric can be found here. You SHOULD 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.