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Unit outline_

COMP5617: Empirical Security Analysis and Engineering

Semester 2, 2024 [Normal evening] - Camperdown/Darlington, Sydney

This unit will present the lessons from recent research and from case studies of practice to bring students the skills to assess and improve the security of deployed systems. A particular focus is on data-driven approaches to collect operational data about a systems security. We explore deployment issues at local and global scale, e. g. for X. 509, DNS, and BGP, and also take human factors explicitly into account. As a result, students will learn to put building blocks of security together in a sound way, to arrive at engineering solutions that are empirically verifiable, functional, and secure against realistic threats. As Dan Geer once famously said: Any security technology whose effectiveness cant be empirically determined is indistinguishable from blind luck.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
INFO3616 or ELEC5616 or CSEC3616 or CSEC5616
Corequisites
? 
None
Prohibitions
? 
COMP4617
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Qiang Tang, qiang.tang@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Open book exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Homework1
short answer questions
10% Week 07
Due date: 11 Sep 2024 at 23:59
3 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Assignment Homework2
short answer questions
10% Week 11
Due date: 09 Oct 2024 at 23:59
3 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
Assignment Project
Design report, code and system documentation
30% Week 13 semester long
Outcomes assessed: LO2 LO8 LO7 LO6 LO5 LO4

Assessment summary

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail 0 - 49 When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Introduction and overview Lecture and tutorial (3 hr) LO2 LO3
Week 02 Cryptography-I Lecture and tutorial (3 hr) LO1 LO2 LO3 LO5 LO7
Week 03 Cryptography-II Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 04 PKI Lecture and tutorial (3 hr) LO2 LO3 LO4 LO5
Week 05 TLS Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 06 DNSSEC Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO7
Week 07 Bitcoin Lecture and tutorial (3 hr) LO2 LO3 LO4 LO5 LO7 LO8
Week 08 Blockchain-II Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Anonymity in Blockchain Lecture and tutorial (3 hr) LO2 LO3 LO4 LO5 LO6 LO8
Week 10 Anonymity and Privacy Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO8
Week 11 Private Computation Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO6 LO7 LO8
Week 12 E2EE messaging Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO6 LO7 LO8
Week 13 Project presentation Lecture and tutorial (3 hr) LO4 LO5 LO7 LO8

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Understand the balance between risk, achieved security, and cost; experience with threat modelling and risk analysis as tools to choose this balance for a given system
  • LO2. Understand common security terminology in security literature
  • LO3. Understand different ways in which security of computer systems can be compromised, e.g. physically, remotely, operationally (esp. social engineering); and relate specific attack scenarios to the major security goals such as authentication, integrity, secrecy, non-repudiation
  • LO4. Understand the major challenges for security of programs, information, computers and networks, and ability to avoid most egregious (typical) flaws in designing and operating IT systems
  • LO5. Demonstrate a high-level knowledge of common approaches to achieve security goals in computer systems, including the main security protocols in the Internet stack
  • LO6. demonstrate knowledge of privacy-preserving technologies
  • LO7. Produce written reports that evaluate a system's security
  • LO8. Research information on security issues from the literature, and analyse a security incident use case

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

minor updates from previous year

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.