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SIT719 - Security and Privacy Issues in Analytics

Year:

2020 unit information

Important Update:

Classes and seminars in Trimester 3, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of trimester.

Last updated: 5 October 2020

Enrolment modes:Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Cloud (online)
Trimester 2: Burwood (Melbourne), Waurn Ponds (Geelong), Cloud (online)
Trimester 3: Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Adnan Anwar
Trimester 3: Rolando Trujillo Rasua
Trimester 2: Adnan Anwar
Prerequisite:

Nil

Corequisite:

Nil

Incompatible with:

Nil

Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus:

2 x 1 hour class per week, 1 x 1-hour workshop each week

 

Scheduled learning activities - cloud:

1 x 1 hour online seminar per week.

Content

The increased size of computer networks has led to the extensive generation of data collected for network defence. A need has arisen for security experts that understand how to build analytics that make use of this data in order to detect or prevent attacks. This unit will provide students with the fundamental tools to understand this area of cyber-security. Students will examine this challenge from multiple perspectives. The unit starts from the basics of building scripts to answer questions of large packet captures as a foundational skillset. Once students are comfortable working with large data sets, they will use this new skill to study several supervised machine learning approaches and apply them to real-world network datasets to build analytics that has been shown to be able to detect various malicious attacks. After becoming comfortable with supervised approaches, students will pivot to examining unsupervised methods for network defence, an important topic, since frequently there are insufficient available examples of malicious behaviour to train good models.

Finally students will study the ethical implications of dealing with large datasets that arise in these contexts by examining privacy attacks that have been developed against large datasets and their associated analytics. All these topics will be explored through scaffolded programming assignments designed to be challenging for a student of any level of programming or mathematical experience. At the end of the unit students will have a solid grounding in how modern analytics work and how they can be applied to network defence.

 

These are the Learning Outcomes (ULO) for this Unit

At the completion of this Unit
successful students can:

Deakin Graduate Learning Outcomes

ULO1

Scripting skills as they relate to large datasets that are encountered in cybersecurity, and the use of popular toolkits used to build analytics.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving

ULO2

Recognise and apply the relevant ethical, regulatory and governance constraints on organisations and professionals when dealing with data and analytics. 

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking

ULO3

Understanding the basics of supervised and unsupervised machine learning algorithms, including their basic mathematical underpinnings, and how they can be implemented using popular libraries.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving

ULO4

Understand how analytics can be used to protect computer networks as well as what types of network defence data might be useful for building analytics. Explore what types of attacks have been successful mitigated by the current state of the art and where work still needs to be done.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking

ULO5

Understand the technical threats to privacy that may result from the use of analytics in cyber security.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking

These Unit Learning Outcomes are applicable for all teaching periods throughout the year

Assessment

Assessment Description Student output Weighting (% total mark for unit) Indicative due week
Learning Portfolio Portfolio consisting of small programs, reflections, reports, and a learning summary report. 80% Weekly task submissions with final submission in Week 12
Examination 2-hour written examination 20% Examination period

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Hurdle requirement

To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio, and must achieve a passing grade in the examination.

Learning Resource

The texts and reading list for the unit can be found on the University Library via the link below: SIT719 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

Unit Fee Information

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