M.Sc. CYBERSECURITY
The MSc in Cybersecurity offers core and elective courses in key security domains, with thesis and non-thesis options.
Program Overview
The Master of Science (MSc) in Cybersecurity program provides a comprehensive exploration of key domains within the cybersecurity field. Through five core courses, students acquire foundational knowledge in network security, applied cryptography, penetration testing, incident response, and information security governance. Elective courses offer opportunities to specialize in high-demand areas such as cloud security, IoT security, AI in cybersecurity, and cybersecurity management. Students may pursue either a thesis or a non-thesis path to complete the degree requirements.

Core Courses
All students must complete the following five core courses:
Course Title |
Credit Hours |
Estimated Learning Hours |
Prerequisites |
Network Security Fundamentals |
3 |
90 |
None |
Applied Cryptography |
3 |
90 |
None |
Ethical Hacking and Penetration Testing |
3 |
90 |
Network Security Fundamentals |
Digital Forensics and Incident Response |
3 |
90 |
Network Security Fundamentals |
Information Security Management and Governance |
3 |
90 |
None |
1. Network Security Fundamentals
Course Content
This course examines the foundational principles of securing computer networks. It introduces students to underlying network architectures and protocols, including TCP/IP, HTTP, DNS, and SMTP, while emphasizing common vulnerabilities and methods of attack such as distributed denial-of-service (DDoS) and man-in-the-middle exploits. Students learn how to configure essential security controls such as firewalls, intrusion detection systems, and virtual private networks. They also become familiar with the process of evaluating different network setups and identifying methods to harden them against intrusion.
Learning Outcomes
By the end of this course, students
should be able to explain the architecture and operation of network protocols,
identify and categorize a variety of network-based attacks, configure basic
network security controls to mitigate threats, and critically assess the
security posture of different network environments.
2. Applied Cryptography
Course Content
This course focuses on the principles
and practical applications of cryptographic mechanisms that protect data in
modern computing environments. Topics include symmetric and asymmetric
encryption algorithms, hashing functions, digital signatures, public key
infrastructures, and cryptographic protocols such as TLS/SSL. Students explore
algorithms like AES, DES, RSA, and ECC, developing an understanding of how cryptographic
methods secure data at rest and in transit. The course also addresses key
management considerations, certificate authorities, and the strengths and
vulnerabilities of each cryptographic technique.
Learning Outcomes
Upon completing this course, students will be able to articulate the foundational concepts of symmetric and asymmetric cryptography, implement various encryption and hashing algorithms to secure information, analyse the suitability of cryptographic protocols for different scenarios, and evaluate digital signature mechanisms as part of an overall security strategy.
3. Ethical Hacking and Penetration Testing
Course Content
In this course, students learn how to
proactively identify system and network vulnerabilities through ethical hacking
and penetration testing methodologies. They explore the legal and ethical
implications of conducting penetration tests and study a range of tools and
techniques used for reconnaissance, scanning, exploitation, and
post-exploitation activities. Practical exercises in controlled laboratory
settings enable students to simulate real-world cyberattacks on virtualized
systems and then document their findings comprehensively.
Learning Outcomes
By the end of this course, students will have the ability to conduct structured penetration tests within legal and ethical boundaries, use industry-standard security tools to discover and exploit vulnerabilities, produce detailed reports on discovered weaknesses, and recommend remediation strategies to strengthen system defences.
4. Digital Forensics and Incident Response
Course Content
This course prepares students to investigate security incidents and conduct digital forensic analyses. It addresses proper evidence collection, preservation, and interpretation in alignment with legal standards. Students examine the incident response lifecycle, including strategies for detection, containment, eradication, and recovery. Case studies and practical exercises demonstrate how forensic tools are used to analyse compromised systems, track malicious activities, and reconstruct the events surrounding a breach.
Learning Outcomes
After completing this course, students
will know how to systematically respond to cybersecurity incidents, follow
established legal and ethical guidelines for handling evidence, utilize
forensic tools to uncover critical details from compromised devices, and
formulate effective incident response plans that mitigate future risks.
5. Information Security Management and Governance
Course Content
This course introduces the core
principles of information security management and governance practices that
organizations use to protect sensitive data. Students learn about risk
assessment, policy development, and regulatory compliance as they craft
strategies to align security measures with business objectives. Topics include
internationally recognized frameworks for information security, business
continuity, disaster recovery planning, and the creation of a security-aware
culture among employees.
Learning Outcomes
Upon completing this course, students
will be able to devise comprehensive information security policies, conduct
systematic risk assessments, propose effective risk mitigation measures,
demonstrate familiarity with relevant laws and standards, and contribute to the
governance and oversight of an organization’s cybersecurity efforts.
Elective Courses
Students must choose at least four electives from the following specialization areas. Each course offers in-depth coverage of advanced topics to cater to diverse career goals.
Specialization Area |
Course Title |
Credit Hours |
Estimated Learning Hours |
Prerequisites |
Cloud Security |
Cloud Security Architecture and Implementation |
3 |
90 |
Network Security Fundamentals |
Cloud Security |
Advanced Topics in Cloud Security |
3 |
90 |
Cloud Security Architecture and Implementation |
IoT Security |
Internet of Things (IoT) Security and Privacy |
3 |
90 |
Network Security Fundamentals |
IoT Security |
Securing Industrial IoT and Cyber-Physical Systems |
3 |
90 |
Internet of Things (IoT) Security and Privacy |
AI in Cybersecurity |
Artificial Intelligence (AI) for Cybersecurity |
3 |
90 |
None (Programming background recommended) |
AI in Cybersecurity |
AI-Powered Threat Analysis and Intelligence |
3 |
90 |
Artificial Intelligence (AI) for Cybersecurity |
Cybersecurity Management |
Cybersecurity Risk Management and Compliance |
3 |
90 |
Information Security Management and Governance |
Cybersecurity Management |
Cybersecurity Leadership and Strategy |
3 |
90 |
Information Security Management and Governance |
1. Cloud Security Architecture and Implementation
Course Content
This course addresses the unique
security challenges in cloud computing environments by examining various
deployment models such as IaaS, PaaS, and SaaS. Students learn how security
responsibilities are divided between cloud providers and their customers under
the shared responsibility model. They also explore methods for configuring
identity and access management, implementing encryption for data at rest and in
transit, and setting up comprehensive monitoring and logging. Real-world case
studies illustrate best practices for securing compute, storage, and networking
services in public and hybrid clouds.
Learning Outcomes
By the end of this course, students should be able to differentiate between common cloud service models, apply relevant security controls for identity and access management, design secure approaches for data protection in cloud environments, and analyse cloud-based security incidents from an architectural perspective.
2. Advanced Topics in Cloud Security
Course Content
This course delves into complex issues
surrounding serverless architectures, container security, and advanced threat
detection in the cloud. Students explore the forensic techniques required to
investigate security incidents across virtualized and serverless environments.
They also learn how to automate security functions and orchestrate responses
using cloud-native tools. Through projects and applied research, students
assess container orchestration platforms like Kubernetes for potential
vulnerabilities, practice secure configurations, and simulate sophisticated
attack scenarios to enhance their defensive capabilities.
Learning Outcomes
Upon course completion, students will
be able to secure serverless and containerized applications in a cloud
environment, perform cloud forensics for complex breaches, implement security
automation strategies, and evaluate cutting-edge technologies for detecting and
responding to cloud-based threats.
3. Internet of Things (IoT) Security and Privacy
Course Content
This course examines the security and
privacy implications of deploying IoT devices that operate through diverse
communication protocols. It covers the unique architectures of IoT ecosystems,
including how MQTT and CoAP enable device-to-device and device-to-cloud
communications. Students investigate widespread vulnerabilities, analyse
real-world IoT attack methods, and practice configuring firmware and network
controls to secure edge devices. The course includes discussions on regulatory
and ethical dimensions of IoT, emphasizing how large-scale adoption can expose
personal data to privacy risks.
Learning Outcomes
By the end of the course, students
will understand the architectural and protocol-level underpinnings of IoT
systems, identify vulnerabilities specific to IoT devices and networks, propose
technical and policy-based interventions to safeguard data, and articulate the
privacy concerns that arise from connecting everyday objects to the internet.
4. Securing Industrial IoT and Cyber-Physical Systems
Course Content
This course focuses on Industrial IoT
(IIoT) and cyber-physical systems that underpin critical infrastructure. It
addresses how legacy systems, such as SCADA and ICS, can converge with modern
network technologies in operational technology (OT) environments. Students
learn techniques to evaluate and mitigate risks in these high-stakes settings,
including the use of intrusion detection and prevention systems designed for OT
networks. Case studies provide examples of past intrusions and highlight best
practices for creating secure remote access, safeguarding industrial protocols,
and ensuring business continuity.
Learning Outcomes
Upon completing this course, students
will understand the specialized security requirements of industrial and
critical infrastructure environments, apply cybersecurity principles to protect
IIoT and OT networks, evaluate risks and vulnerabilities introduced by
convergent IT-OT systems, and propose robust approaches to secure large-scale
industrial deployments.
5. Artificial Intelligence (AI) for Cybersecurity
Course Content
This course explores how artificial
intelligence and machine learning can augment cybersecurity defences. Students
gain a conceptual and practical understanding of AI techniques, including
supervised and unsupervised learning methods for anomaly detection, malware
classification, and predictive vulnerability assessments. They also study the
challenges posed by AI-driven attacks, along with ethical considerations and
potential biases in AI models. Practical assignments require students to
implement basic machine learning algorithms for security applications and
analyse their performance on real or simulated data sets.
Learning Outcomes
By the end of the course, students
will be able to employ AI and machine learning techniques to identify unusual
patterns in network traffic, automate threat intelligence gathering, evaluate
the limitations of algorithmic approaches in detecting advanced attacks, and
discuss the ethical and legal considerations involved in AI-driven security
solutions.
6. AI-Powered Threat Analysis and Intelligence
Course Content
Building on foundational AI concepts,
this course introduces advanced techniques for processing and correlating large
volumes of security data. Students learn to employ big data analytics, natural
language processing, and predictive modelling to detect sophisticated cyber
threats. They also examine how threat intelligence platforms aggregate and
structure data from diverse sources, and they practice generating actionable
insights for proactive defence. The course culminates in projects that simulate
end-to-end threat analysis workflows, from data ingestion to real-time incident
alerts.
Learning Outcomes
Upon course completion, students will
be able to use AI and machine learning for advanced threat intelligence, build
customized data pipelines for analysing security events, predict potential vulnerabilities
or attack vectors, and convert raw data into comprehensive intelligence reports
that inform strategic defensive measures.
7. Cybersecurity Risk Management and Compliance
Course Content
This course provides a deep dive into
risk management frameworks and industry-specific compliance requirements.
Students examine approaches for identifying and assessing cybersecurity risks,
including both qualitative and quantitative methods. They learn to develop and
implement risk treatment strategies—such as mitigation, transference, or
acceptance—and to align these strategies with organizational goals. The course
also reviews major legal and regulatory frameworks, detailing how audits and
certifications ensure compliance with standards like PCI DSS, SOX, and GDPR.
Learning Outcomes
By the end of the course, students
will know how to apply formal risk management methodologies, execute detailed
risk assessments, recommend well-founded responses to identified threats, and
ensure organizational compliance with relevant cybersecurity laws and industry
regulations.
8. Cybersecurity Leadership and Strategy
Course Content
Focusing on the managerial aspects of
cybersecurity, this course teaches students how to design strategies that protect
organizational interests while supporting broader business objectives.
Participants learn how to build and lead high-performing security teams,
communicate risk effectively to executives and stakeholders, and cultivate an
organizational culture that values security practices. Realistic simulations
and team-based projects demonstrate how to respond to large-scale incidents,
measure security performance over time, and coordinate with other business
units to align technical defences with strategic goals.
Learning Outcomes
Upon completing this course, students will possess the skills to create and execute cybersecurity strategies that align with business priorities, oversee and mentor security teams, effectively present cybersecurity risks to senior leadership, and shape a workplace environment that prioritizes proactive security measures.

Graduation Requirements
Students must complete 33 credit hours in total. This includes 15 credits from the five core courses and a minimum of 12 credits from elective courses. There are two options to fulfill the remaining 6 credits:
1. Non-Thesis Option: Students undertake one additional elective (3 credits) and a Capstone Project (3 credits).
2. Thesis Option: Students replace two electives with a Master’s Thesis (6 credits).
In both
options, the total credit requirement remains 33.