Masters Programs

Admission to the Master of Business Administration (MBA) Program

Applicants seeking admission to the Master of Business Administration (MBA) program must satisfy the following requirements:
 

General Admission Requirements

Applicants must:

• Hold a bachelor’s degree from an accredited institution recognized by the Ministry of Higher Education.

• Have a minimum cumulative GPA of 3.00 on a 4.00 scale, or its equivalent.

  • Applicants with a cumulative GPA between 2.67 and 2.99 may be considered for conditional admission in accordance with the University’s graduate admission rules.

• Submit an official transcript from all post-secondary institutions attended (AUK graduates are exempt from submitting an official AUK transcript).

• Submit a current résumé or curriculum vitae (CV).

• Provide two letters of recommendation, at least one of which should be from an academic referee.

• Demonstrate English language proficiency (AUK graduates are exempt from this requirement) by satisfying one of the following:

  • A minimum TOEFL score of 550 (paper-based) or its equivalent.

  • A minimum overall IELTS Academic score of 6.0, with no individual band score below 5.0.

  • Another English language assessment approved by the University, such as the AUK Accuplacer English placement test.

Program-Specific Requirements

• Applicants are expected to hold a bachelor's degree in Accounting, Finance, Economics, Management, Marketing, Human Resource Management, or a closely related discipline

• Applicants holding bachelor’s degrees in other disciplines may be considered for admission. Such applicants may be required to complete one or more undergraduate foundation or prerequisite courses, as determined by the MBA Program Committee, to ensure adequate preparation for graduate-level business studies.

Conditional Admission

Applicants who do not fully satisfy all admission requirements may be considered for conditional admission, subject to the University's graduate admission rules.

Students admitted under conditional admission status may enroll in the program, subject to the conditions specified in their admission offer. They shall remain conditionally admitted until they have satisfied all specified conditions.

Conditions of conditional admission may include, but are not limited to:

• Successful completion of prescribed undergraduate prerequisite or foundation courses.

• Submission of outstanding admission documents.

• Fulfillment of any additional requirements specified by the graduate program or the University.

The Master of Business Administration (MBA) at the American University of Kuwait is a distinctive 36-credit-hour graduate program designed to develop humane, ethical, innovative, technologically agile, and globally minded business leaders. The program emphasizes experiential learning, interdisciplinary thinking, ethical decision-making, sustainability, innovation, entrepreneurship, digital transformation, artificial intelligence, and data-driven management. Students complete 24 credit hours of Core Managerial Operations courses and a further 12 credit hours through the General MBA or a concentration in Business Analytics or International Business. The curriculum integrates rigorous academic study with real-world business projects, case studies, simulations, applied research, teamwork, and industry engagement, preparing graduates to address complex organizational and societal challenges in Kuwait and the global business environment.

The MBA program seeks to develop ethical, innovative, articulate, technologically agile, and globally responsive leaders capable of creating sustainable value for organizations and society through responsible decision-making, entrepreneurship, interdisciplinary business thinking, and effective use of emerging technologies. The program builds on AUK and CBE's mission-driven educational approach and supports Kuwait's economic and social development and Kuwait Vision 2035.

Upon successful completion of the MBA program, graduates will be able to:
 

1. Demonstrate Ethical and Humane Leadership
   Apply critical thinking, ethical judgment, and inclusive leadership practices to address complex organizational challenges in diverse, multicultural, and global environments.

2. Drive Innovation, Entrepreneurship Thinking, and Organizational Change
   Develop innovative solutions, lead change initiatives, and evaluate the strategic and financial implications of entrepreneurial opportunities and organizational transformation.

3. Promote Sustainability and Social Responsibility
   Integrate social, ethical, environmental, and economic considerations into managerial decision-making to create sustainable value for organizations and society.

4. Leverage Technology and Data Analytics for Strategic Decision-Making
   Evaluate emerging technologies, digital trends, and business data to formulate evidence-based strategies that enhance organizational performance while addressing privacy, security, and ethical considerations.

5. Develop and Implement Effective Business Strategies
   Analyze complex business environments and design strategies that improve organizational effectiveness, resilience, competitiveness, and long-term performance.

6. Demonstrate Global and Cultural Agility
   Operate effectively in international and multicultural business environments by recognizing diverse perspectives and adapting leadership and management practices accordingly.

The MBA prepares graduates for leadership and managerial roles across public, private, and nonprofit sectors, including:

• General Manager

• Business Development Manager

• Strategic Planning Manager

• Operations Manager

• Marketing Manager

• Financial Manager

• Corporate Strategy Analyst

• Business Consultant

• Entrepreneurship and Startup Founder

• Project Manager

• Business Analytics Specialist

• International Business Manager

• Executive Leadership Positions

Graduates will be equipped to lead organizational transformation, innovation, sustainability initiatives, and strategic growth.

Total Credits (36 Credit Hours):

• Core Managerial Operations Courses (24)

• General MBA Electives or Concentration Courses (12)

Program Duration

• Minimum 12–18 months of full-time study.

• Up to 24 months of part-time study.

Program Options

• General MBA

• MBA with Concentration in Business Analytics

• MBA with Concentration in International Business

Once the program gains traction, planned future concentrations include Accounting, Finance, Leadership and HRM, and Digital Marketing. 

To earn an MBA degree, students must:

• Complete 36 credit hours prescribed for the degree.

• Complete all required Core Managerial Operations courses (24 credit hours).

• Complete a further 12 credit hours through the General MBA or an approved concentration.

• Students pursuing a concentration must complete four courses (12 credit hours) in the selected concentration.

• Maintain a minimum cumulative GPA of 3.0 (B) at the end of the program, with no more than two courses completed with a letter grade of C.

• All registered non-business students must complete a self-paced MBA preparatory program within the first semester of study.

• Students may register for up to 12 credit hours per semester and 6 credit hours during the summer.

• BUS 503 Strategic Management – Business Simulation must be completed in the last semester of study.

• Meet all applicable institutional graduation requirements.
   

Core Managerial Operations Program (24 Credit Hours)

General MBA Electives (12 Credit Hours)

Students may also choose from prescribed courses offered across the concentrations, subject to program requirements.

Business Analytics Concentration (12 Credit Hours)

International Business Concentration (12 Credit Hours)

 BUS 589 (Special Topics) may be substituted for one concentration course. BUS 590 (Study Abroad and Global Immersion Experience) is an option only for the International Business concentration.

ACCT 501 – Corporate Accounting and Reporting

Develops advanced competencies in financial reporting, accounting and risk analysis, sustainability and integrated reporting, and accounting information systems, with emphasis on organizational performance, value creation, stakeholder confidence, and applications in Kuwait and abroad.

MRKT 501 – Rhetorical Dissonance and Marketing

Examines responsible and sustainable marketing, corporate identity and accountability, digital transformation in marketing, and the social and environmental implications of business practices. Students gain hands-on experience applying marketing techniques to Kuwaiti companies.

FINC 501 – Financial Decision Making

Examines corporate financial decision-making, governance, restructuring, mergers and acquisitions, risk, cost of capital, sustainability, accountability, and ethics through a multidisciplinary approach integrating finance, accounting, law, economics, and business ethics.

MGMT 502 – Organizational Behavior and Leadership

Examines organizational behavior, change management, human behavior, and leadership, emphasizing people as a source of competitive advantage and developing leadership capabilities for effective organizational decision-making and sustainable performance.

BUS 501 – Innovation and Entrepreneurship

Integrates entrepreneurship, innovation, and project management in international value creation. Students develop skills in venture planning, business models, entrepreneurial decision-making, pitching, crowdfunding, and family-business succession in Kuwaiti and global contexts.

BUS 502 – Business Research Methods and Data Analytics

Develops research and data analytics capabilities for in-company projects, reports, and research proposals. Students apply quantitative and qualitative methods, data visualization, and critical evaluation to communicate evidence-based insights to academic and professional audiences.

BUS 503 – Strategic Management – Business Simulation

Provides an advanced exploration of strategic management through real-world cases and hands-on business simulation. Students develop strategic analysis, competitive strategy, governance, executive decision-making, leadership, sustainability, and ESG-focused strategy capabilities.

BUS 504 – Leadership and Corporate Accountability

Examines the economic, legal, and ethical responsibilities of business leaders and the relationship between accountable leadership, resource allocation, wealth creation, and long-term sustainable organizational value.

BUS 505 – Business Negotiations and Deal Making

Examines negotiation, deal making, and conflict resolution in corporate transactions, mergers and acquisitions, entrepreneurship, capital raising, employment agreements, strategic partnerships, and international business through case studies, simulations, and experiential exercises.

BEAL 501 – Legal and Ethical Corporate Conduct

Explores the interaction of law, business, technology, and society in domestic and international settings, including contracts, cyber law, business structures, liability, privacy, employment law, ethics, and the role of business in shaping regulation.

ECON 501 – Behavioral Economics

Challenges traditional assumptions of fully rational decision-making by integrating insights from psychology, anthropology, sociology, politics, and institutional economics to explain economic behavior, managerial choices, sustainability, fairness, and decision-making under uncertainty.

BUS 506 – Digital Transformation and AI Strategy

Examines how digital technologies and artificial intelligence transform organizations, industries, and business models. Topics include digital strategy, disruption, organizational agility, data-driven cultures, cybersecurity, responsible AI, ethical implications, and sustainable competitive advantage.

COMP 515 – Artificial Intelligence

Covers advanced AI concepts including knowledge representation, reasoning, search algorithms, game playing, uncertainty, fuzzy logic, feed-forward neural networks, and evolutionary algorithms. Prerequisite: Data Structures and Discrete Mathematics.

COMP 502 – Big Data Analytics

Covers the end-to-end big-data analytics process, including data management, storage, retrieval, representation, knowledge extraction, visualization, statistical analysis, and hands-on programming with real-world datasets using tools such as Python or R. Prerequisite: Artificial Intelligence.

COMP 403 – Cybersecurity

Introduces information security and assurance, risk assessment, cyber threats, cyberattacks, mitigation strategies, cybersecurity technologies, and relevant public policy, legal, and ethical standards. A significant research project is required. Prerequisite: Computer Networks.

IR 522 – International Business and Bargaining

Introduces international business, trade and bargaining theories, governmental influences on trade, currency exchange, international finance, international organizations, multinational corporations, and the sociocultural, economic, technological, and political-legal dimensions of global markets.

IR 525 – International Trade

Analyzes the causes and consequences of international trade and investment, including why nations trade, distributional gains and losses, multinational and foreign direct investment, international trade agreements, and current trade policy disputes.

IR 506 – International Political Economy

Examines cross-border flows of goods, capital, and people and related topics such as trade policy, foreign investment, exchange-rate policy, migration, development, macroeconomic policy, international institutions, and inequality using micro- and macro-level evidence.
 

• Real-world business projects addressing challenges relevant to Kuwait.

• Experiential learning through simulations, case studies, applied projects, teamwork, and industry engagement.

• Strategic Management capstone integrating theories, skills, and tools across the MBA curriculum through business simulation.

• Strong emphasis on humane and ethical leadership, innovation and entrepreneurship, sustainability, technological agility, AI, and data-driven decision-making.

• Multidisciplinary learning designed to prepare students for leadership across private, public, and nonprofit organizations.

• Curriculum designed to contribute to Kuwait's economic and social development and the aspirations of Kuwait Vision 2035.

Admission to the Master of Applied Computer Science (MACS) Program

Applicants seeking admission to the Master of Applied Computer Science (MACS) program must satisfy the following requirements:
 

General Admission Requirements

Applicants must:

• Hold a bachelor’s degree from an accredited institution recognized by the Ministry of Higher Education.

• Have a minimum cumulative GPA of 3.00 on a 4.00 scale, or its equivalent.

  • Applicants with a cumulative GPA between 2.67 and 2.99 may be considered for conditional admission in accordance with the University’s graduate admission policies.

• Submit an official transcript from all post-secondary institutions attended (AUK graduates are exempt from submitting an official AUK transcript).

• Submit a current résumé or curriculum vitae (CV).

• Provide two letters of recommendation, at least one of which should be from an academic referee.

• Demonstrate English language proficiency (AUK graduates are exempt from this requirement) by satisfying one of the following:

  • A minimum TOEFL score of 550 (paper-based) or its equivalent.

  • A minimum overall IELTS Academic score of 6.0, with no individual band score below 5.0.

  • Another English language assessment approved by the University, such as the AUK Accuplacer English placement test.

Program-Specific Requirements

• Applicants are expected to hold a bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Information Systems, or another closely related computing discipline.

• Applicants with degrees in other disciplines may be considered for admission if they demonstrate adequate preparation in computing fundamentals. Such applicants may be required to complete undergraduate prerequisite courses in areas such as programming, data structures, algorithms, computer architecture, operating systems, databases, discrete mathematics, or other subjects deemed necessary by the MACS Program Committee before commencing graduate studies.

Conditional Admission

Applicants who do not fully satisfy all admission requirements may be considered for conditional admission, subject to the University's graduate admission rules.

Students admitted under conditional admission status may enroll in the program, subject to the conditions specified in their admission offer. They shall remain conditionally admitted until they have satisfied all specified conditions.

Conditions of conditional admission may include, but are not limited to:

• Successful completion of prescribed undergraduate prerequisite or foundation courses.

• Submission of outstanding admission documents.

• Fulfillment of any additional requirements specified by the graduate program or the University.

The College of Engineering and Applied Sciences (CEAS) offers five undergraduate programs in Computer Engineering, Electrical Engineering, Systems Engineering, Computer Science and Information Systems. Three of the current programs are ABET accredited, namely: Computer Science, Computer Engineering and Electrical Engineering.

Stemming from AUK’s vision and mission, the College of Engineering and Applied Sciences (CEAS) at AUK is proposing a new program of Master of Science in Applied Computer Science – MACS. It is expected that this program will attract a wide base of students due to its flexibility and diversity in the different tracks it offers. CEAS is planning to optimize the resources by including faculty members from both Computing and Engineering to teach and supervise graduate students enrolled in the MACS program.

The MACS program will provide students with fundamental knowledge, skills and research of applied computer science in one of the four essential areas of Big Data and Artificial Intelligence, Cybersecurity, Software Engineering, and Networking. Such offering ensures students success in their respective future endeavors in the IT industry or further postgraduate studies.

The program is typically completed in under two years, 9 credits per semester. The program has two options: Thesis (30 credits) and Non-thesis (33 credits). For the thesis option, course work consists of 12 core credits common for all tracks, 12 elective credits per track, and a 6-credits thesis. For the non-thesis option, course work consists of 12 core credits common for all tracks, 18 elective credits per track, and a 3-credits project. Both options include a 0-credit graduate seminar course.

It is anticipated that AUK and CEAS will benefit from the MACS program in three ways: the MACS program will support and expand AUK’s and CEAS’s current teaching and research profiles, AUK will contribute more to the community and receive more recognition in return by creating greater interaction among its faculty through multidisciplinary research; and enrollment numbers will increase and more students will be attracted to join AUK as we provide a master degree program.

The MACS program has four different tracks: Big Data and Artificial Intelligence, Cybersecurity, Software Engineering, and Networking. Courses will be designed to include activities of multidisciplinary nature through real world applications and case studies. This in turn can lead to multidisciplinary research in the thesis. The program can be completed with either a thesis or a project option. The tracks offered by the proposed MACS are aligned with global trends and offer more focused courses in each track. Many universities offer postgraduate programs that are specific for certain fields (e.g. Master in Cybersecurity), these are represented as tracks under a single master’s program. These tracks provide students with specialised information in specific domains, which can better prepare students for their thesis work or in future PhD studies.

All following information is provided based on the assumption of classroom education, however, should the current circumstances continue, we now have the experience to do what’s necessary to accommodate the existing situations.

Inspired by CEAS vision. The Program Educational Objectives (PEOs) of MACS are as follows:

• Gain solid knowledge in the fundamentals of applied computer science beyond the undergraduate level.

• Gain specialized knowledge and practical skills in one of the offered tracks in order to solve current and future regional and world challenging problems.

• Identify research directions, design a research plan, conduct research experiments, and analyze experimental data.

• Function ethically and responsibly in the profession and society.

• Able to communicate and disseminate knowledge.

Additional Student Learning Outcomes (Track Specific) are as follows:

Big Data and Artificial Intelligence

• Demonstrate mastery of the body of knowledge in Big Data and Artificial Intelligence.

• An ability to apply Artificial Intelligence techniques to design, implement and test related applications.

• Demonstrate advanced skills in using big data analysis, modeling and visualization tools.

• An ability to conduct scientific research in Big Data and Artificial Intelligence.

  
   

Cybersecurity

• Demonstrate mastery of the body of knowledge in Cybersecurity.

• An ability to apply Cybersecurity techniques to design, implement and test secure systems.

• Demonstrate advanced skills in using cybersecurity analysis, detection and prevention tools.

• An ability to conduct scientific research in Cybersecurity.

  
   

• Bachelor of Science in Computer Science, Computer Engineering or a closely related field from an ABET accredited program with a minimum GPA of 3.0.

• An official hardcopy transcript (not required for AUK graduates).

• A resume (or a complete CV).

• Two letters of references (at least one from an academic source).

• English proficiency requirement (AUK graduates are exempted from this requirement):

  • A minimum TOEFL score of 70.

  • A minimum overall score of 6.0 in IELTS without having an individual score of less than 5.0.

• Conditional admission: applicants may have to take some of AUK undergraduate courses to satisfy prerequisite requirements. Applicants will be advised about the prerequisite requirements prior to admittance. Conditional acceptance could be provided for students with GPA between 2.7 and 3.0. Based on our observation, many AUK students with GPA in this range would cherish the opportunity of enrolling in a master’s program.

The proposed M.Sc. program has two options:

• A 30 credit hours thesis option that is divided as follows:

  • Core courses: 12 credit hours (4 courses 3 credit hours each),

  • Electives: 12 credit hours for thesis option (4 courses 3 credit hours each),

  • A seminar course: 0 credit hours, and

  • Thesis: 6 credit hours.

• A 33 credit hours non-thesis option that is divided as follows:

  • Core courses: 12 credit hours (4 courses 3 credit hours each),

  • Electives: 18 credit hours for non-thesis option (6 courses 3 credit hours each),

  • A seminar course: 0 credit hours, and

  • Project (non-thesis option): 3 credit hours.

The pool of core courses is as follows:

• COMP 515: Artificial Intelligence.

• COMP 530: Advanced Software Engineering.

• COMP 503: Cybersecurity.

• COMP 505: Advanced Algorithms Design.

• COMP 540: Advanced Computer Networks.

• COMP 502: Graduate Seminar.

  
   

Pool of Elective Courses per Track

The program, in either option, could be completed over a minimum duration of 20 months as shown in the tables below:

Proposed Plan of Study (Thesis option – 30 credits)

Proposed Plan of Study (Non-Thesis option – 33 credits)

The graduation requirements of the programs are as follows:

• A minimum letter grade of C is required to pass each course.

• A minimum cumulative GPA of 3.0 must be maintained at the end of the program with no more than two courses completed with a letter grade of C.

COMP 515: Artificial Intelligence

The objective of this course is to provide students with advanced concepts in the field of artificial intelligence. The course covers the following topics: theoretical aspects in knowledge representation, reasoning, search algorithms, game playing, uncertainty, fuzzy logic, feed-forward neural networks, and evolutionary algorithms.
Prerequisite: Data Structures and Discrete Mathematics.
 

COMP 530: Advanced Software Engineering

This course is deigned to introduce students to advanced and contemporary software engineering topics. It builds upon previous experiences in undergraduate course in the topic. Advanced Object-Oriented software engineering topics will be covered including: design patterns, testing, project management, and metrics to measure quality of code. In addition, the course will also introduce the challenges of distributed software development. Students have to develop small framework, document it and use it to develop at least one application. Advanced activities such as graduate-level research are included.
Prerequisite: Software Engineering.
 

COMP 503: Cybersecurity

This course introduces the fundamental of information security concepts; a basic introduction to information assurance principles and information security systems and specific issues pertaining to risk assessment and cyber threats; a basic introduction on cyberattacks and mitigation strategies, and cybersecurity technologies; a brief examination of the laws governing information security including public policy and ethical standards. A significant research project is required.
Prerequisite: Computer Networks.
 

COMP 505: Advanced Algorithms Design

This course introduces advanced topics in analysis and design of algorithms. Different topics will include linear programming, dynamic programming, randomization, and approximation algorithms. NP-complete problems will be studied. The course introduces comparison of the runtime efficiency of solutions using different strategies. Space and time efficiency are discussed, compared and analyzed for different types of algorithms.
Prerequisite: Analysis and Design of Algorithms.
 

COMP 540: Advanced Computer Networks

This course will cover advanced principles of computer networks. These topics include, internetworking with IPv6, routing technologies, congestion control, internetwork QoS, network security, self-organizing networks, and software-defined networks (SDN). Students will be also exposed to the principles of network performance evaluation using mathematical modelling and network simulation tools.
Prerequisite: Computer Networks.
 

COMP 502: Graduate Seminar

The objective of this course is to prepare students to do their thesis/project. Students will have to present a public seminar in the area of their research track.
Prerequisite: Successful completion of 18 credits (including core courses).
 

COMP 506: Introduction to Big Data

The course presents students with an overview of big data and the phases of analyzing big data as an end-to-end process. The course explores the topics of data management, storing, retrieval, analysis and extracting new knowledge from data. This includes topics such as big data analysis, systems for storing big data, and data representation. Students learn how to use open-source software (such as Python or R) for data analysis which includes: collecting data (e.g., web scraping), processing data, visualizing data, and statistical data analysis. This course involves hands-on programming assignments using real-world datasets.
Prerequisite: Artificial Intelligence.
 

COMP 538: Neural Networks and Deep Learning

The course provides an in-depth understanding of neural networks and deep learning including the designing, training, adapting, and testing neural networks. Students learn about fundamental concepts and algorithms behind neural networks, including backpropagation, algorithmic differentiation, regularization, and distributed representations. Introduce the commonly used neural network architectures such as perceptrons, convolutional, recurrent, and long/short term memory neural networks. Students get practical experience in building neural networks.
Prerequisite: Artificial Intelligence.
 

COMP 535: Machine Learning and Pattern Recognition

The objective of this course is to make students understand and apply the primary algorithms used in learning systems. Topics include supervised learning; classification, regression, decision trees, rule induction, nearest neighbors, Bayesian methods, support vector machines, model ensembles. Unsupervised learning; clustering.
Prerequisite: Artificial Intelligence.
 

COMP 504: Data Mining

The objective of this course is to introduce students to the fundamental theory and practice of data mining in databases. Students learn about data pre-processing, data classification, data clustering, data warehousing and online analytical processing, association mining, and visual data exploration. Students will work on projects using open-source statistics and data-mining software such as R and Weka.
Prerequisite: Artificial Intelligence.
 

COMP 545: Cloud Computing

The course presents state-of-the-art cloud computing technologies and applications. Providing practical project opportunities using different technologies. Students learn about the problems and challenges encountered when cultivating a cloud computing system. Topics include telecommunications necessity; architectural models for cloud computing; cloud computing platforms and services; security, privacy, and trust management; big data system architecture; and novel applications.
Prerequisite: Computer Networks.
 

COMP 520: Distributed Big Data Processing

The goal of this course is to teach students how to build distributed processing systems for big data. Students practice state-of-the-art open-source technologies such as Apache Hadoop and its ecosystem. Students study map-reduce, streaming analysis, and external memory algorithms. Students learn the design principles for building a large network computing system that assists data-intensive computing such as recommender systems, stream processing, efficient search in large textual collections and social network analysis. Students examine and analyze large existing databases.
Prerequisite: Introduction to Big Data.
 

COMP 525: Big Data Analytics

The course focuses on how to use big data analytics to make better decisions. Students acquire skills to manipulate and analyze the big amount of data. This course provides students with a hands-on approach for data collection, processing, analysis, mining, visualization, prediction, and interpretation of the results. Students incorporate methods from statistical data analysis, data mining, and machine learning. They work on a project that deals with large data sets and solves real-world problems. For example, social network analysis, credit risk management, sentiment analysis, airline safety, predicting the weather, and spam filtering.
Prerequisite: Introduction to Big Data.
 

COMP 600: Special Topics in Artificial Intelligence

This course is open for any advanced topic that fits the four tracks of MACS and can be repeated for credit with a different topic. Advanced activities such as graduate-level research are included.
Perquisites: Permission of Instructor.
 

COMP 572: Cryptography

This course introduces computational methods that provides secure communication. Topics include: Security threats in communication systems; conventional cryptography: substitution and transposition codes; fundamentals of public-key cryptography; RSA and other public-key methods; and digital signatures.
Prerequisite: Cybersecurity.
 

COMP 511: Ethical Hacking

This course introduces different hacking techniques. The course also introduces different countermeasures. Students will have hands on experience by doing group projects that simulate cyber-attacks.
Prerequisite: Cybersecurity.
 

COMP 500: Cybersecurity in the Middle East: Laws, Policies, and Regulations

This course presents legal and ethical issues related to Cybersecurity in the middle east. Laws, policies, and regulations from different regions will be compared and analyzed.
Prerequisite: Cybersecurity.
 

COMP 526: Digital Forensics

The objective of this course is to introduce the techniques for digital forensics investigation. Different tools will be examined. Reviewing and analyzing forensics reports will be studied through case studies.
Prerequisite: Cybersecurity.
 

COMP 550: Advanced Network Security

This course presents fundamentals security principles and real-world applications of Internet and computer security. Topics include legal and privacy issues, risk analysis, attack and intrusion detection and intrusion prevention concepts, system log analysis, packet filtering techniques, computer security models, computer forensics, and distributed denial-of-service (DDoS) attacks.
Prerequisite: Computer Networks.
 

COMP 580: Secure Programming

The course introduces the fundamentals of secure programming; a basic introduction to writing secure codes by avoiding vulnerabilities or potential security holes; incorporating defensive programming concepts into the cycle of software development; familiarization with security features available in programming languages libraries, comparison of security vulnerabilities in known programming languages (like C, Java, or Python); the course includes a major project on developing secure codes for real-life applications and systems.
Perquisites: Computer Programming.
 

COMP 602: Special Topics in Cybersecurity

This course is open for any advanced computer science topic that fits the four tracks of MACS and can be repeated for credit with a different topic. Advanced activities such as graduate-level research are included.
Perquisites: Permission of Instructor.
 

COMP 610: Thesis I

This course is the first stage in the required research work in MACS. In this course, students are required to identify a thesis major advisor, area of interest in their respective track, compile a literature review, write a formal proposal, deliver a presentation, defend the proposal and get the proposal approved by supervisor. The committee should include (in addition to the supervisor) at least one computing faculty, one from Engineering faculty, one external examiner (not from AUK).
Prerequisite: Successful completion of 18 credits (including core courses).
 

COMP 620: Thesis II

This course is the second stage in the required research work in MACS. In this course, students are required to work according to their proposal in Thesis I, finish all their research work, complete formal write-up of their thesis, prepare thesis presentation, defend the thesis, get the thesis approved by thesis committee.
Prerequisite: Thesis I.
 

COMP 605: Project

This course will include developing a project in the chosen track under the supervision of advisor.
Prerequisite: Successful completion of 24 credits (including core courses).