INFORMATION SECURITY

Course Description

In our digital modern world, computer-based applications of internet, database and information technology has raised security related issues such as confidentiality, integrity and availability of information. Information security is concerned with identification, authentication and access control. In this course, a broad knowledge of information security technologies and tools is provided. Students shall be able to understand advanced concepts in assessing the strengths and limitations of information security, integrity and privacy techniques. By completion of this course, students will gain a broad understanding of various types of security incidents and attacks, as well as different techniques to prevent, detect and react such attacks. Students will also be able to evaluate the performance of different security systems, and assess each system’s limitations and vulnerabilities.

INFORMATION SECURITY

Evaluation Tools (Active Term)

No evaluation items have been defined.

Course outcomes

1. 01 At the end of the course, Students shall be able to understand the key concepts of information security and how they work
2. 02 At the end of the course, Students shall be able to understand the foundational theory behind information security
3. 03 At the end of the course, Students shall be able to o design, develop, manage, and analyze security systems
4. 04 At the end of the course, Students shall be able to point out common threat and vulnerabilities.

Course Syllabus

Reference Books & Course Materials

1. 01 Principles of Information Security, 3rd Edition by Micheal Whitman and Herbert Mattord
2. 02 Security in Computing, 3rd Edition by Charles P. Pfleeger and Shari Lawrence Pfleeger

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

1. Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
2. Review the literature and apply data science theories and methodology in new research and experiments.
3. Analyze datasets using supervised and unsupervised machine learning techniques.
4. Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
5. Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
6. Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
7. Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
8. Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
9. Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
10. Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

Po-Lo Matrix

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