CS498 Special Topics in Computer Science I

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
CS498 Special Topics in Computer Science I 3 2 6
Prerequisite None It is a prerequisite to

None

Lecturer Ali Almisreb Office Hours / Room / Phone
Monday:
9:00-10:00
Thursday:
13:00-17:00
Friday:
9:00-11:00
Saturday:
8:00-18:00 Via Teams
Sunday:
8:00-18:00 Via Teams
A F2.6
E-mail aalmisreb@ius.edu.ba
Assistant Assistant E-mail
Course Objectives This course aims to:
1. Describe the field of digital forensics.
2. Describe certification requirements for digital forensics labs.
3. Learn the data acquisition over multiple storage media.
4. Learn how to process digital crime and incident scenes.
5. Provide the ability to work with different digital forensics tools and operating systems.
Textbook Basic Literature: [1] Bill Nelson, Amelia Phillips, Christopher Steuart. Guide to Computer Forensics and Investigations: Processing Digital Evidence / Cengage Learning, Inc., 6th edition, 2019. (Required).
Additional Literature
  • Additional Reading:
  • Kävrestad, Joakim. Guide to digital forensics: A concise and practical introduction. Springer, 2017.
  • Årnes, André, ed. Digital forensics. John Wiley & Sons, 2017.
  • Nikkel, Bruce. Practical forensic imaging: securing digital evidence with Linux tools. No Starch Press, 2016.
  • Hosmer, Chet. Integrating Python with Leading Computer Forensics Platforms. Syngress, 2016.
  • Kävrestad, Joakim. Fundamentals of Digital Forensics: Theory, Methods, and Real-Life Applications. Springer Nature, 2020.
  • Hassan, Nihad A. Digital Forensics Basics: A Practical Guide Using Windows OS. Apress, 2019.
  • Oettinger, William. Learn Computer Forensics: A beginner's guide to searching, analyzing, and securing digital evidence. Packt Publishing Ltd, 2020.
  • Xiaodong Lin. Introductory Computer Forensics A Hands-on Practical Approach. Springer Nature Switzerland AG, 2018.
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Explain requirements for data recovery workstations and software.
  2. Describe components used to build a business case for developing a forensics lab.
  3. Explain ways to determine the best acquisition method.
  4. Explain guidelines for seizing digital evidence at the scene.
  5. Describe available digital forensics software tools.
  6. Explain how to locate and recover graphics files.
Teaching Methods The course will be divided, logically, into 3 sections, the first section will introduce the theory of the topic, then be followed with practical in-class examples with digital forensics tools
Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
WEEK TOPIC REFERENCE
Week 1 Understanding the Digital Forensics Profession and Investigations Chapter 1
Week 2 The Investigator’s Office and Laboratory Chapter 2
Week 3 Data Acquisition Chapter 3
Week 4 Processing Crime and Incident Scenes Chapter 4
Week 5 Working with Windows and CLI Systems Chapter 5
Week 6 Current Digital Forensics Tools Chapter 6
Week 7 Mid-Term Exam
Week 8 Recovering Graphics Files Chapter 7
Week 9 Digital Forensics Analysis and Validation Chapter 8
Week 10 Virtual Machine Forensics, Live Acquisitions, and Network Forensics Chapter 9
Week 11 E-mail and Social Media Investigations Chapter 10
Week 12 Mobile Device Forensics and the Internet of Anything Chapter 11
Week 13 Cloud Forensics Chapter 12
Week 14 Report Writing for High-Tech Investigations Chapter 13
Week 15 Expert Testimony in Digital Investigations & Ethics for the Expert Witness Chapter 14
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam / Final Project 1 35 3
Semester Evaluation Components
Midterm 1 25 1,5
Labs 15 30 2,4
Quizzes 2 10
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture Hours 3 15 45 Active Tutorials 2 14 28
Home Study 4 14 56 In-term Exam Study 8 1 8
Final Exam Study 13 1 13
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 23/10/2023
QR Code for https://ecampus.ius.edu.ba/syllabus/cs498-special-topics-computer-science-i

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