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CIS5206 Data Mining for Business Analytics and Cyber Security

Semester 2, 2021 On-campus Springfield
Short Description: Data Mining:Bus Anlytc Cyb Sec
Units : 1
Faculty or Section : Faculty of Business, Education, Law and Arts
School or Department : School of Business
Student contribution band : Band 2
ASCED code : 020305 - Systems Analysis and Design
Grading basis : Graded
Version produced : 11 April 2021


Detecting and preventing cyber-attacks becomes increasingly important in order to protect businesses from cyber terrorism threats. Data mining techniques can be effective in tackling cyber security challenges. This course provides critical knowledge required to understand the application of data mining to cyber threats in order to protect businesses from any unforeseen cyber-attacks.


This course provides an overview of the different types of cyber-attacks, the business systems that are most at risk, and the strengths and challenges of data mining approach to cybersecurity. The course will cover data mining algorithms including prediction, classification, clustering mechanisms, and association rules, which have all been used to discover and generalize attack patterns so as to develop powerful business solutions for dealing with the threats. Students will also learn various applications of data mining that can be utilised in the real-time detection of threats. The course also provides an opportunity for hands-on experimentation with applying data mining to real-life security problems in the practical workshop with real-world data.


On successful completion of this course students should be able to:

  1. examine the processes associated with the use of data mining tools in the context of cybersecurity;
  2. synthesise and articulate a wide range of cybersecurity applications to data mining;
  3. critically assess data mining & data analytics to address business process threats as a result of cyberattack;
  4. make informed decisions based on high level knowledge and skills about data mining techniques to realworld cybersecurity applications;
  5. analyse the strengths and challenges of data mining techniques to detect cyber security incidents in business environments.


Description Weighting(%)
1. Introduction to data mining and analytics for cyber security 10.00
2. Data mining techniques 20.00
3. Network and system security 10.00
4. Data mining for malware and intrusion and fraud detection 20.00
5. Social network security 10.00
6. Email: Spam detection, Phishing detection 15.00
7. Internet of Things (IoT)/Infrastructure security 15.00

Text and materials required to be purchased or accessed

ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (

Please contact us for alternative purchase options from USQ Bookshop. (

There are no texts or materials required for this course.

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.

Student workload expectations

Activity Hours
Assessments 30.00
Private Study 135.00

Assessment details

Description Marks out of Wtg (%) Due Date Objectives Assessed Notes
ASSIGNMENT 1 2 10 28 Jul 2021 1,4,6 (see note 1)
ASSIGNMENT 2 100 25 03 Sep 2021 1,2,3,4 (see note 2)
ASSIGNMENT 3 100 25 15 Oct 2021 1,2,3,5 (see note 3)
ONLINE EXAM 100 40 End S2 1,2,3,4

  1. A take home exercise each week for 2 marks. There are 5 weeks from week 3 to 7. In total, assignment 1 weighted for 2x5=10%
  2. Case Study
  3. A literature report

Important assessment information

  1. Attendance requirements:
    It is the students' responsibility to attend and participate appropriately in all activities scheduled for them, and to study all material provided to them or required to be accessed by them, to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.

  3. Penalties for late submission of required work:
    Students should refer to the Assessment Procedure (point 4.2.4)

  4. Requirements for student to be awarded a passing grade in the course:
    To be assured of receiving a passing grade a student must obtain at least 50% of the total weighted marks available for the course (i.e. the Primary Hurdle).

  5. Method used to combine assessment results to attain final grade:
    The final grades for students will be assigned on the basis of the aggregate of the weighted marks obtained for each of the summative items for the course.

  6. Examination information:
    Candidates are allowed to bring only writing and drawing instruments into a closed examination.

  7. Examination period when Deferred/Supplementary examinations will be held:
    Any Deferred or Supplementary examinations for this course will be held during the next examination period.

  8. University Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at

Assessment notes

  1. Referencing in assignments must comply with the Harvard (AGPS) referencing system. This system should be used by students to format details of the information sources they have cited in their work. The Harvard (APGS) style to be used is defined by the USQ library’s referencing guide. This guide can be found at

Evaluation and benchmarking

In meeting the University’s aims to establish quality learning and teaching for all programs, this course monitors and ensures quality assurance and improvements in at least two ways. This course:

Conforms to the USQ Policy on Evaluation of Teaching, Courses and Programs to ensure ongoing monitoring and systematic improvement and is benchmarked against the internal USQ accreditation/reaccreditation processes which include (i) stringent standards in the independent accreditation of its academic programs, (ii) close integration between business and academic planning, and (iii) regular and rigorous review.

Date printed 11 April 2021