CURRICULUM

Security Technologies: Students learn in projects the fundamental technologies of security. The level of mastery is tested in the project presentation.

The course content consists of these main topics:

1. Introduction to Information Security 

2. The Need for Security

3. Legal, Ethical, and Professional Issues in Information Security,

4. Planning for Security, 

5. Risk Management,

6. Security Technology: Access Controls, Firewalls, and VPNs,

7. Security Technology: Intrusion Detection and Prevention Systems, and other security tools, 

8. Cryptography,

9. Physical Security,

10. Implementing Information Security, 

11. Security and Personnel, 

12. Information Security Maintenance.

The competence goals of this course are: 

Personal competence: Assessing own competence.

Social competence: Work in new aspects of computer science, work out and present solutions in teams and understanding Social Engineering.

Cloud Solutions: Technology refers to the ability to use distributed hardware and software resources provided by a provider on the Internet on demand and to pay for them based on usage. This course introduces concepts (e.g., "Everything-as-a-Service", virtualisation) of cloud computing and some cloud architectures, cloud offerings, programming models, software tools and applications developed in recent years. Economic considerations, as well as opportunities and risks of cloud computing, will be explained. One focus of the course is on practical testing of the concepts taught in the form of programming exercises in an IoT background.

The course content consists of these main topics:

1. Cloud Computing Basics,

2. Framework and Terminology, 

3. Case Studies,

4. Cloud Solution Architect,

5. Amazon AWS, Google 

6. Essential Cloud Infrastructure: Google, Amazon, Microsoft, 

7. Docker Technologies 

8. Compartmentalising web applications, 

9. Cloud Monitoring and SysOps, 

10. Monitoring EBS, RDS, ELB, EC2, Elasticcache,

11. High-availability, Deployment & provisioning Opsworks,

12. Data management Security, networking with Route53, VPC, 

13. Serverless.

The competence goals of this course are:

Professional competence: Students will be able to independently solve technical programming tasks and problems in a cloud as well as meet the changing technical demands of the profession. 

Methodological competence: Students are able to handle the processing of professional requirements, tasks and activities and proceed in work in a targeted, structured and effective manner. 

Personal competence: The students are well trained in oral communication skills in the exercises by practising free speech in front of an audience and during discussion. They are able to assess their strengths and weaknesses by means of exercises and to organise and optimise their time and learning management. 

Social competence: Students will be able to communicate and justify their own approaches to solutions within the group structure but also be enthusiastic about the solution in the group and work on it profitably.

Open-Source Intelligence: This module looks at how we can store, manipulate, and analyse big data. We define big data essentially as data that is nonatomic and is not well suited to tabular storage and manipulation.

It is important to ensure that the students can understand why these two broad classes of data really are different and why they warrant such different treatment. The course content consists of these main topics:

1. This Advanced OSINT Lab consists of several exercises that start with setting up a working environment and teach how to work efficiently in it., Afterwards, freely available tools are presented, and the use of these tools is trained. This is followed by the presentation of the most common commercial tools, which are worked out step by step in the individual exercises. Finally, it is shown how all these tools can be used to aggregate search results from many individual data.

2. Linux Fundamentals for OSINT: Linux Shell Fundamentals, – Linux File System Hierarchy Fundamentals, – Linux Pipe Fundamentals. OSINT Basics; – Advanced Google Search, – Linux Networking Tools, – Linux Forensics Tools. OSINT Commercial Tools; – Hacking-Lab Environment Preparation, – PassiveTotal, – Censys, – Shodan, – Maltego.

The competence goals of this course are:

Professional competence: The students have a comprehensive overview of the topic of Open-Source Intelligence. It allows them to work independently with the newly taught tools, collect data from freely available sources, and aggregate it into investigation results. They are well prepared for the requirements of the profession and can use acquired knowledge profitably. 

Methodological competence: Students are able to recognise and name possible solutions based on given tasks. They are able to work out suitable solutions and apply the professional knowledge they have acquired. Students are able to solve complex problems and transfer them to other situations. Students apply networked and abstract thinking and analyse their solution approach before and after solving the problem. 

Personal competence: Students are able to coordinate their working methods and time management in a concrete and complex project environment and act in a self-reliant and independent way. 

Social competence: Students are able to work on complex tasks in a self-reflective way and discuss solutions constructively in the group, and defend them in a diplomatic manner.

Cybersecurity Management Project: Students will be able to independently manage a cybersecurity project and gain expertise in the terminology. Information Security terminology: Security targets, threats, vulnerabilities, risks, security controls, management systems, Introduction in the Information Security Management Systems (ISMS) based on the standards family ISO 27000, Identification, assessment and treatment of typical risks in information systems, Typical security measures in distributed information systems, in particular in web-based systems Special fields of interest, e.g., malware control, firewalls systems hardening, encryption technologies, cyberwar, cybersecurity, auditing and reviewing information security, business continuity management, Darknet, network security etc., Passwords & biometrics, Introduction to cryptography, Sessions, SSL/TLS, Certificates, electronic signatures, public key infrastructures, Side-channel analysis, Access control, Privacy.

IT Security Management & DevOps: The aim of the module is to equip the students with the analytical skills and knowledge to assess security in large systems and organisations and to incorporate appropriate levels of security in the various steps of a system's lifecycle.

The course content consists of these main topics: 

1. Introduction to the Management of Information Security, 

2. Compliance: Law and Ethics, 

3. Governance and Strategic Planning for Security, 

4. Information Security Policy, 

5. Developing the Security Program, 

6. Risk Management: Assessing Risk

7. Risk Management: Treating Risk, 

8. Security Management Models, 

9. Security Management Practices, 

10. Planning for Contingencies, 

11. Security Maintenance, 

12. Protection Mechanisms, 

13. Devsope.

The competence goals of this course are: 

Professional competence: Identify and discuss the benefits of embedding security throughout an organisation; Understand how to relate and adapt information systems in general and security solutions in particular to specific business processes and requirements to meet overall goals.

Methodological competence: Methods of IT Operations and Software Development (holistic).

Personal competence: Be able to communicate clearly and unambiguously about security problems to other people in an organisation; Be able to identify assets and threats and assess risks; Assess own competence. 

Social competence: Students are able to discuss several fundamental IT Security Management problems in the group to bring about an agreement process and to share responsibility for this. 

IT-Revision und Audit: In an interactive seminar, the students learn the principles of IT revision and audit and have to show their level of mastery in a portfolio exam consisting of different tasks. The course content consists of these main topics:

1. Auditing and Internal Control,

2. Auditing IT Governance Controls,

3. Security Part I: Auditing Operating Systems and Networks, 

4. Security Part II: Auditing Database Systems,

5. Systems Development and Program Change Activities,

6. Transaction Processing and Financial Reporting Systems Overview, 

7. Computer-Assisted Audit Tools and Techniques,

8. Data Structures and CAATTs for Data Extraction, 

9. Auditing the Revenue Cycle,

10. Auditing the Expenditure Cycle,

11. Enterprise Resource Planning Systems,

12. Business Ethics, Fraud, and Fraud Detection

The competence goals of this course are:

Professional competence: Students will be able to describe all steps of an information security auditing process for IT systems / IACS / processes. The students know all the essential steps/phases of the auditing process and can apply auditing processes to IT systems and processes. The students know the essential requirements for an auditing process of the relevant standards. Students will be able to perform audits for an object of investigation (IT system, part of an IT system, process). 

Methodological competence: Students can select the correct type and appropriate procedure of an audit for an object of investigation (IT system, part of an IT system, process) and evaluate the criticality of identified deficiencies. Students will be able to assess whether certain measures are suitable to remedy or alleviate identified deficiencies / weaknesses / findings. 

Personal competence: Through the exercises that take place, students are encouraged to work out issues independently and present them in a comprehensible manner. 

Social competence: Students perform audits on case studies as a team in changing roles. Through this collaboration, the knowledge and skills of other students are experienced as helpful and beneficial.

Long-Term Practical Training: This course is one of the areas of specialisation that can be selected in the first semester of the 2nd year. SRH will connect students with industrial partners for long-term practical work. In this case, a representative from the industry might be involved in students’ thesis defence examination. This might be confidential should the industrial partner request so. Professors in SRH have part-time Professor positions in SRH University and hold management and research positions in big companies (Price Waterhouse Coopers, Ernst & Young, Siemens, etc.). This ensures good cooperation with different companies for long-term practical training and technology transfer, internship and master thesis work, and job opportunities for students.

Advanced Data Technologies: With the interplay of lectures and exercises, the students develop the knowledge of state-of-the-art methods of Artificial Intelligence for problem-solving. The level of mastery is tested with a written exam.

The course content consists of these main topics:

1. Exploring Data, Regression, 

2. Linear Regression Multiple Regression, 

3. Non-linear Regression Assessing Model Accuracy, 

4. Classification, Logistic Regression, 

5. K-Nearest Neighbors Naive Bayes, 

6. Decision Trees,

7. Random Forests, 

8. Unsupervised Learning K-Means Clustering Hierarchical Clustering, Using R.

The competence goals of this course are: 

Professional competence: Students know methods for data exploration like regression, classification and unsupervised learning as the methodological basis for Artificial Intelligence. 

Methodological competence: Students are able to explore data by applying common methods like regression, classification, or unsupervised learning. They develop problem-oriented as well as abstract and joined-up thinking. They are able to apply the methods in practice. Personal competence: plan, organise and prioritise their work efficiently and effectively; critically reflect on their work and results; communicate their ideas in class; held group meetings and demonstrate their results. 

Social competence: effectively work in teams; take on team responsibilities; receive and discuss constructive feedback; analyze and transform requirements into feasible tasks.

Machine Learning: Machine learning is a field of scientific study concerned with algorithmic techniques that enable machines to learn performance on a given task via the discovery of patterns or regularities in exemplary data. Consequently, its methods commonly draw upon a statistical basis in conjunction with the computational capabilities of modern computing hardware. This course aims to acquaint the student with the main branches of machine learning and provide a thorough introduction to the most widely used approaches and methods in this field.

The course content consists of these main topics: 

1. Introduction to Machine Learning,

2. Supervised and Unsupervised learning,

3. Clustering, 

4. Linear Regression with One Variable,

5. Linear Algebra Review,

6. Linear Regression with Multiple Variables, 

7. Logistic Regression,

8. Regularisation, 

9. Predictive and Classification Algorithms, 

10. SVM, k-NN, Decision trees, Random Forests, 

11. Convolution, 

12. Neural Networks, CNNs, 

13. Applying Machine Learning: Natural Language Processing.

The competence goals of this course are:

Professional competence: On successful completion, students will be able to learn different machine learning model classes; comprehend the difference between supervised, unsupervised, and reinforcement learning methods; understand common machine learning models; analyse trade-offs in the application of different models; appropriately choose machine learning models according to a given task. 

Methodological competence: The students understand the statistical foundations of generalisation, i.e., the induction of models from data, as well as practical tools for model validation. They are able to apply basic methods of supervised learning to problems of classification and regression. The students have an overview of methods for multi-class classification, the learning of nonlinear models, and extensions of the simple setting of supervised learning. They understand algorithmic concepts of corresponding methods and are able to apply them to real problems.

Personal competence: Students will be able to communicate and justify their approaches to solutions within the group structure but also be enthusiastic about the solution in the group and work on it profitably. 

Social competence: Students work in teams, present their results and reflect their results in the group.

Artificial Intelligence: With the interplay of lectures and exercises, students develop the ability to apply methods of Artificial Intelligence to different use cases. The level of mastery is tested with the written exam.

The course content consists of these main topics:

1. Support Vector Machines, ROC Curves,

2. Neural Networks, Deep Learning Projects Using R or similar language,

The competence goals of this course are:

Professional competence: Students know typical methods of Artificial Intelligence like Neural Networks, Deep Learning, ROC curves and how to implement these methods in a programming language like R or Python. 

Methodological competence: Students are able to analyse problems of industrial automation and digital systems by using methods of Artificial Intelligence. They develop problem-oriented as well as abstract and joined-up thinking. They are able to apply the methods in practice. 

Personal competence: Students are able to plan, organise and prioritise their work efficiently and effectively; critically reflect on their work and results; communicate their ideas in class; held group meetings; and present their results. 

Social competence: Students are able to effectively work in teams; take on responsibilities in teams; receive and discuss constructive feedback; analyse and transform requirements into feasible tasks.

Big Data and Business Intelligence: This module looks at how we can store, manipulate, and analyse big data. We define big data essentially as data that is non-atomic and is not well suited to tabular storage and manipulation. It is important to ensure that the students can understand why these two broad classes of data really are different and why they warrant such different treatment. The course content consists of these main topics: 

1. Introduction to Big Data,

2. Big Data Modelling and Management Systems, 

3. Big Data Integration and Processing, -Machine Learning with Big Data,

4. Graph Analytics with Big Data, 

5. Big Data Project.

The competence goals of this course are: 

Professional competence: Describe the purpose and uses of Business Intelligence & Big Data in the business world today; identify the terminology used in Big Data and quantitative analysis programs in general. 

Methodological competence: Build a dataset based on gathering data from multiple sources and merging those databases into a single unified set; clean a database through automated methods like winsorizing and evaluation of univariate metrics to determine the accuracy of inputs; identify key risk issues involved in Big Data and the role that information governance plays. 

Personal competence: Be able to communicate clearly and unambiguously about Big Data and Business Intelligence problems to other people in an organisation; be able to identify assets and threats and assess risks; assess own competence.

Advanced Security Technologies: Students learn in projects the fundamental technologies of security. The level of mastery is tested in the project presentation. The course content consists of these main topics:

1. Introduction to Information Security,

2. The Need for Security, 

3. Legal, Ethical, and Professional Issues in Information Security,

4. Planning for Security, 

5. Risk Management, 

6. Security Technology: Access Controls, Firewalls, and VPNs,

7. Security Technology: Intrusion Detection and Prevention Systems, and other security tools, 

8. Cryptography, 

9. Physical Security, 

10. Implementing Information Security, 

11. Security and Personnel, 

12. Information Security Maintenance.

The competence goals of this course are:

Professional competence: On completing the module successfully, the students will be able to locate typical security flaws and vulnerabilities in distributed applications; formulate high-level security policies for practical case studies; identify risks and appropriate risk-reducing security measures. 

Methodological competence: Perform a security analysis following accepted standards; identify, document, and reason appropriate security controls. 

Personal competence: Assessing own competence. 

Social competence: Work in new aspects of computer science; work out and present solutions in teams; understand Social Engineering.