1/24 Dr. Robert Layton Internet Commerce Security Laboratory Centre for Informatics and Applied Optimisation University of Ballarat Mathematics and Cybercrime

2/24 Who am I?  Bachelor of Computing, Major in Mathematics (2007)  Bachelor of Applied Computing (first class honours) (2008)  UB Council – Student Member (2009)  PhD (2011), ICSL  Postdoc, ICSL  Early Career Researcher

3/24 Overview - What I learnt during my Bachelor. - How I learnt it. - What worked, what did not. - How I apply that today in my research.

4/24 Early On - Always good at Maths. - Natural to continue that in Uni (good grades, less essay writing). - All electives were Maths related, ran out of subjects to do, so started doing guided studies.

5/24 Game Theory Early unit on game theory. Introduction to game state trees. Example based teaching, able to see the outcomes quickly. This comes in handy later.

6/24 Modelling Reality - Differential Equations - A significant leap in difficulty for me. - Good communication from lecturer helped significantly. - High Distinction for subject, giving a huge confidence boost for future subjects. Prabhu Manyem

7/24 Linear Programming Another difficult subject, but introduced practical applications of matrices, which have become incredibly important later on.

8/24 Automata and Context-Free Languages

9/24 Combinatorial Optimisation - A branch and bound approach to integrated circuit design. - NP-hard problem, related to the 2- dimensional Bin Packing problem. - Simulation created based optimisation for placement of the IC chips. - Constraint based optimisation using an approximation algorithm to be able to solve in reasonable time.

10/24 Topology Guided study with David Yost, using the book “Topology without Tears” by our ex-head of school, Sid Morris. Defined the concept of metric spaces for me, which helps later.

11/24 Artificial Intelligence - Implemented a program for playing Reversi/Othello - Searching the game state tree using the minimax algorithm with alpha-beta pruning - Depth first search for memory conservation

12/24 Neural Networks - Taught by Richard Dazeley. - Heavy experimental approach – code it and see how it works! - Fitted my learning style really well. - Introduction to Data Mining

13/24 Data Mining Applied a data mining approach to poker. Then went with an artificial intelligence approach based on statistical stereotyping of opponent play. Honours Project

14/24 My thoughts on my undergraduate education. Interest Matters 1) Getting students to understand why, rather than what, helps. 2) Goal-based work - “Develop a program that uses minimax to play a computer game” 3) Get people's hands dirty – examples are needed, not only proofs but applications.

15/24 My Research – in numbers >$1,088,000 in successful grants - NeCTAR - >$1M (still confidential) - AFACT - $25,000 + $15,000 - Google - $15,000 - APWG - $ Other - $30,000 (distributions matter!) Supervision – complete: honours - 1 undergraduate guided study Supervision – in progress: full time PhD - 4 part time PhD - 1 full time Masters by Research - 1 undergraduate guided study 20 publications - 4 in Journal of Natural Language Engineering - 2 in other journals - 14 conference papers - 15 co-authors across five institutions

16/24 My Research Goal Automated – From start to finish Unsupervised – No training set Authorship – Who wrote what? Analysis – How does that help us?

17/24 Impact – Phishing Attacks

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19/24 Educational Underpinnings Without a solid background in Mathematics, my research findings to date would not have been as effective.

20/24 Statistics: Distributions Matter

21/24 Topology: Distance Metrics

22/24 Modelling: Temporal Analysis

23/24 More on Temporal Analysis

24/24 Lessons I learnt - Blind Experimentation will only get you so far - Understanding the application, rather than “black boxing” it will net better results. - Learning how algorithms work is more efficient in the long run. - Learn widely – concepts from other fields will help where you least expect it.