Professionals based in industry who have interest in cybersecurity. The micro credential will prepare professionals for a rewarding career; for example, to take a role as a security manager, security engineer, security analyst, or IT security specialist.
Malware Analysis
Overview
| Module Code | COMP47810 |
| Module Title | Malware Analysis |
| Subject Area | Cybersecurity |
| Credits | 5 |
| NFQ | 9 |
| EFQ | 7 |
| Start Date | 20th January 2025 |
| Duration | 12 Weeks |
| Time | Synchronous |
| Mode of Delivery | Blended |
| Course Leader | Dr Anca Delia Jurcut |
| Fee | Full Fee: €875 *Subsidised Fee:€175 *80% Learner Fee Subsidy for eligible learners (Please see below) |
| Application Deadline | 13th January 2025 |
This micro-credential introduces the different types of malware (malware taxonomy) and the existing methods to detect and analyse malware. It teaches methods to identify and analyse malware samples using static and dynamic analysis, machine learning and reverse engineering techniques. Furthermore, key reverse engineering tools such as IDA Pro and OllyDbg are introduced.
On successful completion of this micro credential a learner should be able:
- Describe types of malware, including Viruses, Worms, Trojans, Rootkits, Spyware and Ransomware
Perform static and dynamic malware analysis on various malware samples
Understand executable formats
Learn to apply machine learning techniques for detection and analysis of malware
Apply techniques and concepts to unpack, extract, and decrypt malware
Common approaches to reverse engineering
Practical skills with industry-standard malware analysis tools.
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Topics will cover:
• Fundamentals of Malware Analysis including: the types of malware, the existing malware analysis techniques and malware analysis tools.
• Static Analysis including: file signature analysis, identifying file dependencies, database of file hashes, string analysis, malware sandboxing, levels of abstraction, x86 assembly, and static analysis tools.
• Dynamic Analysis including: debugging, source level vs. assembly level debuggers, Kernel vs. user-mode debugging, DLL analysis, and dynamic analysis tools.
• Reverse Engineering including: reverse engineering malicious code, identifying malware passwords, bypassing authentication, advanced malware analysis: - case study: Ransomware analysis using ML techniques - and reverse engineering tools: IDA Pro and Ollydbg.
• Malware Functionality including: malware behavior, covert malware launching, data encoding, and malware-focused network signatures.
• Anti-Reverse-Engineering including: anti-disassembly, anti-debugging, packers, and unpacking.
• Machine Learning Techniques for Malware Analysis including: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and Deep Learning techniques.
This holistic and well-rounded course will be of interest to executives and professional/technical staff who:
- need to acquire knowledge and skills to equip themselves better for their current role.
- want to gain a promotion.
- want to open up new career opportunities for themselves in cyber security related roles.
- are interested in doing some research in this field, perhaps in relation to a current work problem.
- want to gain a qualification to add to their standing and credibility within their professional life.
Approaches to Teaching and Learning:
The course material will be delivered as weekly live lectures that will be recorded and made available for students to view afterwards. The lectures will be complemented by 3 face to face workshops (remote option available) where the malware analysis tools used in this course are introduced (first workshop), the assignments are explained and discussed (workshop 2), the assignments are presented and evaluated (workshop 3).
Minimum of 2.1 honours bachelor’s degree in Computer Science (or a cognate discipline) or 2:2 honours bachelor’s degree in computer science (or a cognate discipline) and equivalent (> 5 years) industrial experience in software development or software/system security.
Each applicant will be assessed on a case-by-case basis. Applicants whose first language is not English must also demonstrate English language proficiency of IELTS 7.0 (no band less than 6.5 in each element), or equivalent.
Individual Project: An individual project that includes 3 phases: proposal, to write an essay based on the results of the work and to do a 10 min presentation of the work (Week 4, 10,12); Exam Online 14
Feedback Strategy/Strategies
- Feedback individually to students, on an activity or draft prior to summative assessment
- Feedback individually to students, post-assessment
- Online automated feedback
Assignment results will be notified after submission deadline. Where appropriate (e.g. when answering MCQ tests) the results will be communicated automatically online.
- Leadership in Security COMP47800
- Applied Cryptography COMP47890
- Risk Assessment and Standards COMP47900
- Secure Software Engineering COMP47910
- Information Security COMP47920
- Cybersecurity Law LAW42160
- Incident Response COMP47870
- Network Security COMP47880
- Malware Analysis COMP47810
- Ethical Hacking COMP47860
Please note: Learners can avail of only one form of funding per application.
Micro-Credentials Learner Fee Subsidy-Human Capital Initiative Pillar 3
The HCI Pillar 3 Micro-credential Learner Fee Subsidy has been introduced to enable more learners to address critical skills gaps and engage with lifelong learning through micro-credentials. The HCI Pillar 3 Micro-credential Learner Fee Subsidy is funded by Higher Education Authority (HEA) and the Department of Further and Higher Education, Research, Innovation and Science.
HCI Micro-credential Learner Fee Subsidies are available on identified micro-credentials only and in fixed numbers from March 2024 until October 2025.
Please see Eligibility Criteria for further information.
If you have any questions about this micro-credential, or would like to speak to a UCD staff member, please contact (opens in a new window)microcredentials@ucd.ie.