Online Master of Cybersecurity: Course Structure

The Academic Integrity Module will introduce you to academic integrity standards, so you’re informed about how to avoid plagiarism and academic misconduct.  You’ll complete four parts that cover academic misconduct and academic integrity decisions, such as cheating, plagiarism and collusion.  You’ll learn about the text-matching tool, Turnitin, that is used at La Trobe, how to get help and where to go to develop referencing skills.

In this subject, you will learn computer system organization and its associated topics. It covers the hardware components of the computer, data storage and retrieval, and introduces system software, computer networks, data communications, the Internet, operating systems, file management systems and security. You will also be introduced to information systems and application software packages.

This subject starts with an overview of the architecture and management of database systems, and a discussion of different existing database models. The main focus includes relational database analysis, design, and implementation. The students learn: relational algebra as the formal foundation of relational databases; relational conceptual design using an entity-relationship diagram; relational logical database design; security and integrity; and SQL implementation of relational database queries. Students will also learn advanced normalization theory and the techniques to remove data anomalies and redundancies. In this subject, students are required to design a database application that meets the needs of a system requirement specification, and to implement the system using a commercial standard database system such as ORACLE or POSTGRESQL. In addition, a selection of advanced topics in databases will be introduced and discussed.

In the Internet era, industries and organisations need to be aware of, and be prepared to defend against threats and attacks. Stakeholders should be familiar with the basic principles and best practices of cyber security to better protect their businesses. In this subject, the principles, the state of the art, and strategies for the future of cyber security is explored thoroughly. The topics will focus on information security, ethical and legal practices, mitigating cyber vulnerabilities, and the process of incident response and analysis. The outline of the subject is targeted at ensuring the privacy, reliability, confidentiality and integrity of information systems. Cyber security is a very broad discipline, and therefore, this subject is only intended to cover the basics of the recent state of the art and leading cyber security topics.

This subject introduces you to the ideals and practices of secure programming. Students begin by learning a procedural language, including the concepts of pre-processor, compiler, functions, control structures (branching and looping), pointers and arrays, structures, and file I/O. Students then learn to identify and analyse common coding practices that lead to security vulnerabilities, such as buffer overflows, SQL injection and Cross Site Scripting (XSS) attacks. Finally, students return to coding, learning to use secure coding techniques and strategies to avoid security vulnerabilities. This subject does not require prior knowledge of computer programming.

This subject explores the motivations, mindset and techniques used by hackers. Although their activities are illicit and illegal, hackers have a finely attuned understanding of computer networks and systems and how users/customers behave in online environments. If nothing else, they have developed new – albeit illegal and unethical business models that exploit vulnerabilities in computer networks and systems. By looking at systems and practices through the eyes of a hacker, you can better identify weaknesses, emerging threats and develop more effective defences.

In this subject we introduce the architecture, structure, functions, components, and models of the Internet and other computer networks. We also look at OSI and TCP/IP layer models to examine the nature and roles of protocols and services at the application, network, data link, and physical layers. The fundamentals of IP addressing, and basic concepts of Ethernet will also be studied.

This subject provides necessary skills and techniques to manage large-scale information technology projects, with strong focus on the analytical side of project management, referring to scheduling, cost, and resource management, as well as the ‘people’ and client management issues that must be dealt with in order to ensure successful projects. Students learn to design Information Technology projects covering network management or software development or data science for efficiency, portability and re-use, as well as to take advantage of different standards and system utilities, data and information management techniques.

The purpose of this subject is to outline the basic principles of Entrepreneurship. It will examine the steps required in developing an idea into a business and will explore the tools and necessary insights to make a successful venture. The subject will involve theory, case studies and guest speakers on start-up issues, pitfalls, and ingredients for success. Students will also develop professional skills related to ethical and moral decision making and evaluate the social implications of their work and the broader global context. The subject requires active participation in group discussions and activities.

Artificial Intelligence (AI) is the field of engaging computers for reasoning and decision-making. In this subject, you will be introduced to fundamental concepts and different application fields of AI. Main topics include searching, knowledge representation and reasoning, expert system design and development, responsible AI principles and applications. Practice on design and development of AI models for real world problems will be offered in labs.

Deep learning is currently the central machine learning method fuelling the artificial intelligence revolution. In this subject you learn how to apply deep learning algorithms to solve real-world problems. This subject does not assume you have previous machine learning experience, therefore it starts teaching deep learning at a very introductory level. You learn how deep learning techniques can be applied to such tasks as image recognition, sentiment classification, machine translation, question and answering, speech synthesis, etc. The practical skills taught in this subject will allow you to build production level deep learning software that can scale out to millions of users. You will be introduced to the popular deep learning programming frameworks of Pytorch and Tensorflow and advanced deep learning techniques such as reinforcement learning, generative adversarial networks and few shot learning.

Quantitative analysis plays an important role in industrial data analytics and knowledge engineering, which makes it very useful to develop computing skills for data regression and classification. This subject covers fundamentals of machine learning techniques in theory and practice. The subject is designed to focus on solving industrial data modelling problems using neural networks. You will learn how to test various learning algorithms and compare performance evaluations. Some advanced machine learning techniques for data classification will also be addressed. You will work with industrial data modelling in labs and assignments to consolidate your knowledge and gain hands-on experience with machine learning applications.

Natural Language Processing (NLP) is broadly concerned with the interactions between computers and natural (i.e., human) languages; more particularly, it is concerned with the question of how to program computers to process and analyse large amounts of natural language data. Following a review of the essential mathematical and linguistic concepts underlying natural language processing, you will develop skills in important natural language processing sub-tasks including accessing corpora, tokenisation, morphological analysis, word sense disambiguation, part-of speech tagging, and analysing sentence structure. You will then apply these skills in the context of applications such as text categorisation, text clustering, text recommendation, and information retrieval. Where appropriate, both lexical (i.e. dictionary-based) and machine learning approaches will be used.

Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. In this subject, you will be introduced to topics in computer vision, covering from early vision to mid and high-level vision such as camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, scene understanding and image captioning. You will practice statistical models and machine learning models for various computer vision tasks. You will have the opportunity to implement algorithms for real-world computer vision applications in labs.

The explosive growth of digital media data has imposed unprecedented challenges for big multimedia data computing and management. In this subject, you will be introduced to a broad range concepts in image computing and retrieval for multimedia database management. You will learn and apply both the basics of digital image computing. Cutting-edge techniques for multimedia data understanding, content analysis, and image retrieval are practiced, applied, and discussed. With learnt knowledge and techniques, you have the skills to implement an image retrieval model.

Students undertake research, across both CSE5001 and CSE5TSB, that takes the equivalent of eight or nine months of continuous work under the supervision of a member of staff. In the first semester, a literature review is written up and submitted as a hurdle requirement for the subject. A list of prospective thesis topics is available from the Department of Computer Science and Information Technology.

This subject introduces you to the ideals and practices of secure programming. Students begin by learning a procedural language, including the concepts of pre-processor, compiler, functions, control structures (branching and looping), pointers and arrays, structures, and file I/O. Students then learn to identify and analyse common coding practices that lead to security vulnerabilities, such as buffer overflows, SQL injection and Cross Site Scripting (XSS) attacks. Finally, students return to coding, learning to use secure coding techniques and strategies to avoid security vulnerabilities. This subject does not require prior knowledge of computer programming.

This subject begins with an overview of security attacks on system and network services, and a discussion of different existing security mechanisms. The main focus includes cryptography and its application in systems, networks and web security. The students learn: (1) cryptographic algorithms and protocols, underlying network security applications including single-key and public-key encryption methods, hash functions, digital signatures and key exchange; (2) intrusion detection systems and firewalls that can be used to protect a computer system from security threats, such as intruders, viruses, and worm; (3) the use of cryptographic algorithms and security protocols for providing network and internet security in terms of user authentication/identification, IP security and Web security. Students will also learn advanced information security through research papers, including mathematical cryptanalysis on single-key and public-key encryptional algorithms.

Penetration testing involves assessment of organisational vulnerabilities through the design and execution of technical system tests. This subject introduces students to the principles and processes involved in system penetration testing. It examines common software tools used in a penetration testing exercise. Students will learn various types of penetration testing and their phases, and the interpretation of results from commonly used penetration testing tools. Students will learn of the value of penetration testing for businesses and organisations, and how to use testing results to report on, and to improve, an organisation’s security resilience.

This subject introduces students to the procedures related to computer forensics and digital investigations including formal case management and evidential best practices. The subject will start with an overview of operating system architectures, data structures and file systems. Students will explore the key principles associated with digital forensic processes, data hiding, evidence collection and validation required to perform forensic analysis. The subject will also cover how to conduct technical forensic processes that comply with legal requirements and documentation for forensic evidence, and the use of practical forensic tools.

Students undertake research, across both CSE5001 and CSE5TSB, that takes the equivalent of eight or nine months of continuous work under the supervision of a member of staff. In the first semester, a literature review is written up and submitted as a hurdle requirement for the subject. A list of prospective thesis topics is available from the Department of Computer Science and Information Technology.

In this subject, students will learn the art and science of incident response. Students will develop business continuity plans, and assess how these can support business operations during cyber incidents. Students will learn key tools and approaches for attacker identification and attribution, including the role played by law enforcement, vendors and government in critical infrastructure protection.

In this subject, students will learn how to create and execute frameworks for cyber security governance, based on an understanding of business strategy and risk appetite. Students will become familiar with standards and frameworks commonly used to ensure that business goals can be achieved in a secure way. Students will learn how to identify appropriate roles and responsibilities to support the security function.

In this subject, students will explore the alignment between business and cyber strategy, and how vertical functions in organisations can support effective responses. Students will review common cyber technologies and emerging trends in responses, including awareness, training and education. Students will become familiar with security metrics and frameworks that can be used to assess the effectiveness of security controls.

This subject introduces students to the principles and processes involved in blockchain technologies. The blockchain offers a way to secure transactions online between two parties, when there is no trusted intermediary available. A common use is in financial transactions without a bank as an intermediary, such as bitcoins and other cryptocurrencies. This subject covers the fundamentals of blockchain technology, including how the blockchain works, and how it is applied to modern digital transactions, including cryptocurrencies and smart contracts.

Students undertake research, across both CSE5001 and CSE5TSB, that takes the equivalent of eight or nine months of continuous work under the supervision of a member of staff. In the first semester, a literature review is written up and submitted as a hurdle requirement for the subject. A list of prospective thesis topics is available from the Department of Computer Science and Information Technology.

This subject develops an understanding of probability and statistics applied to Data Science. Probability topics include joint and conditional probability, Bayes’ Theorem and distributions such as the uniform, binomial, Poisson and normal distributions as well as properties of random variables and the Central Limit Theorem. Statistical inference and data analysis is also considered covering, among other topics, significance testing and confidence intervals with an introduction to methods such as ANOVA, linear and nonlinear regression and model verification. Applications to data science are considered and students will be exposed to the R statistical package as well as the mathematical type-setting package LaTeX.