Logistics

Class Mondays and Wednesdays, 6:00 PM--7:20 PM Mountain Time (MT)
Location College of Business Administration 323
Format Approximately 40% lecture and 60% hands-on and in-class activities
Office Hours Tuesdays, 3:00 PM--4:00 PM Mountain Time (MT)
Course Website sre.rahmanmsaidur.com
IQSeC SRELab srelab.rahmanmsaidur.com

Communication. Post general course questions on the Microsoft Teams channel so the answers can benefit the class. Email the instructor for private or individual matters.

Academic accommodations. Students who require accommodations should contact the Center for Accommodations and Support Services (CASS) as early as possible. Approved accommodations will be provided according to the student's CASS accommodation letter.

Academic integrity. Academic integrity is required in all course activities and assessments. See Academic Integrity below.

Team

Saidur Rahman
Saidur Rahman
Instructor

Overview

Course Overview

This course combines hands-on practice with research in software reverse engineering, malware analysis, and AI security. Students will learn how to analyze software and malware, understand malicious behavior, and identify techniques used to avoid detection. The course uses tools such as Wireshark, Ghidra, IDA Pro, and related malware analysis platforms. Students will also explore automated reverse engineering using AI and large language models (LLMs).

The course also covers intelligent malware analysis using machine learning and introduces AI red teaming for identifying weaknesses in AI systems. Students will read recent security research, analyze current problems, and apply what they learn through labs, presentations, and a semester project.

Why Software Reverse Engineering?

Software reverse engineering helps us understand how software works when its source code is unavailable. It is widely used to analyze malware, find vulnerabilities, investigate attacks, and understand unknown or suspicious software.

AI-Assisted Reverse Engineering

AI and LLMs can help explain code, automate parts of program analysis, and support malware investigation. We will study both their capabilities and limitations.

Intelligent Malware Analysis

Students will use machine learning to study malware features, build detection and classification models, and evaluate their performance.

AI Red Teaming

Students will learn basic methods for testing AI systems, identifying security weaknesses, and evaluating possible defenses.

Prerequisites

Students are expected to have the following background:

Academic Integrity

Academic dishonesty is prohibited and is considered a violation of the UTEP Handbook of Operating Procedures (HOOP). It includes cheating, plagiarism, and collusion.

Suspected violations must be reported to the Office of Community Standards. See the UTEP student conduct and discipline policy for details.

Use outside resources responsibly. If you are unsure whether something is allowed, ask the instructor before submitting your work.

Allowed

  • Search for information and ask public questions about the systems studied in the course, with proper citation.
  • Discuss questions and ideas with classmates. Disclose discussion partners when required.
  • Use existing solutions as part of a project when they are properly cited and your own contribution is clearly identified.
  • Publish your final project after the course.

Not Allowed

  • Copy solutions from AI tools, websites, or other sources and submit them as your own.
  • Ask another person to complete your assignments, labs, or project.
  • Copy solutions from classmates.
  • Present someone else's work as your own.
  • Post course assignment solutions online during or after the course.

Guidance on Artificial Intelligence

Generative AI tools such as ChatGPT or Gemini may be useful for brainstorming and learning, but they can produce incorrect information and false citations. You may not submit AI-generated work as your own.

If you use information or materials produced by an AI tool, cite the tool and disclose how it was used. When feasible, include a link to the relevant AI session or history. Directly submitting AI-generated material as your own work will be treated as plagiarism and may be reported to the Office of Community Standards.

Audit Policy

UTEP students and staff may request to audit the course. Auditors may attend lectures and access public course materials, but they will not receive grades or feedback on labs, assignments, or projects. Contact the Computer Science department to arrange an audit. External audit requests cannot be accommodated because the course is taught in person.

Reference Resources

The course does not require a textbook. The following books are optional references: