Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers

379 Pages·2018·20.245 MB·English

by  Mohadeseh Ganji, Lida Rashidi, Benjamin C. M. Fung, Can Wang

Mohadeseh Ganji · Lida Rashidi
Benjamin C. M. Fung · Can Wang (Eds.)
Trends and Applications
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PAKDD 2018 Workshops, BDASC,
BDM, ML4Cyber, PAISI, DaMEMO
Melbourne, VIC, Australia, June 3, 2018
Revised Selected Papers
123
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Lecture Notes in Arti cial Intelligence 11154
Subseries of Lecture Notes in Computer Science
LNAI Series Editors
Randy Goebel
University of Alberta, Edmonton, Canada
Yuzuru Tanaka
Hokkaido University, Sapporo, Japan
Wolfgang Wahlster
DFKI and Saarland University, Saarbrücken, Germany
LNAI Founding Series Editor
Joerg Siekmann
DFKI and Saarland University, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/1244
Mohadeseh Ganji Lida Rashidi
(cid:129)
Benjamin C. M. Fung Can Wang (Eds.)
(cid:129)
Trends and Applications
in Knowledge Discovery
and Data Mining
PAKDD 2018 Workshops, BDASC,
BDM, ML4Cyber, PAISI, DaMEMO
Melbourne, VIC, Australia, June 3, 2018
Revised Selected Papers
123
Editors
MohadesehGanji Benjamin C. M.Fung
University of Melbourne McGill University
Melbourne, VIC,Australia Montreal,QC, Canada
LidaRashidi CanWang
University of Melbourne GriffithUniversity
Melbourne, VIC,Australia GoldCoast, QLD,Australia
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Artificial Intelligence
ISBN 978-3-030-04502-9 ISBN978-3-030-04503-6 (eBook)
https://doi.org/10.1007/978-3-030-04503-6
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Preface
OnJune3,2018,fiveworkshopswerehostedinconjunctionwiththe22ndPacific-Asia
ConferenceonKnowledgeDiscoveryandDataMining(PAKDD2018)inMelbourne,
Australia. The five workshops were the Pacific Asia Workshop on Intelligence and
Security Informatics (PAISI), Workshop on Biologically Inspired Techniques for
KnowledgeDiscoveryandDataMining(BDM),theWorkshoponBigDataAnalytics
for Social Computing (BDASC), Data Mining for Energy Modeling and Optimization
(DaMEMO), and the Australasian Workshop on Machine Learning for Cyber-security
(ML4Cyber). This volume contains selected papers from five workshops. These
workshops provided an informal and vibrant opportunity for researchers and industry
practitioners to share their research positions, original research results, and practical
development experiences on specific challenges and emerging issues. The workshop
topics are focused and cohesive so that participants can benefit from interaction with
each other.
InthePAKDD2018workshops,each submitted paper was rigorouslyreviewed by
at least two Program Committee members. The workshop organizers received many
high-quality publications, butonly 32papers could be acceptedfor presentation atthe
workshopsandpublicationsinthisvolume.OnbehalfofthePAKDD2018Organizing
Committee,wewouldliketosincerelythanktheworkshoporganizersfortheirefforts,
resources,andtimetosuccessfullydelivertheworkshops.Wewouldalsoliketothank
the authors for submitting their high-quality work to the PAKDD workshops, the
presenters for preparing the presentations and sharing their valuable findings, and the
participantsfortheirefforttoattendtheworkshopsinMelbourne.Weareconfidentthat
the presenters and participants alike benefited from the in-person interactions and
fruitful discussions at the workshops.
WewouldalsoliketoespeciallythankthePCchair,Prof.DinhPhung,forpatiently
answering our long e-mails, and the publications chairs, Dr. Mohadeseh Ganji and
Dr. Lida Rashidi, for arranging this volume with the publisher.
June 2018 Benjamin C. M. Fung
Can Wang
ML4CYBER 2018 Workshop Program Chairs’ Message
Machine learning solutions are now seen as key to providing effective cyber security.
A wide range of techniques are used by both researchers and commercial security
solutions.
This research includes the use of commonly employed approaches including
supervised learning and unsupervised learning.
However, cyber security problems raise challenges to machine learning. The chal-
lenges include the problems associated with correctly labelling data, the adversarial
natureofsecurityproblems,andforsupervisedtaskstheasymmetrybetweenlegitimate
andmaliciousactivity.Furthermore,maliciousattackershavebeenwitnessedtousethe
machinelearning approach toadvance their knowledge and capacities. The aim ofthe
workshop is to highlight current research in the cyber security space.
ML4CYBER2018attracted19submissions,andnineofthemwereacceptedaftera
single-blind review by at least two reviewers. The overall acceptance rate for the
workshop was 47%.
Theselectedpaperslookedatadiverserangeofsecurityproblems,rangingfromthe
whitelisting problem, intrusion detection, spam to specific security issues looking at
malware that performed webinjects and securing the network on a smart car. The
acceptedpapersusedarangeoftechniquesrangingfromtheclassicapproachessuchas
decision trees and support vector machines to deep learning.
Wearethankfultotheresearcherswhomadethisworkshoppossiblebysubmitting
their work. We congratulate the authors of the submissions who won prizes. We are
also thankful to our Program Committee, who provided reviews in a professional and
timely way, and the sponsors, the University of Waikato, Trendmicro, and
Data61/CSIRO,whoprovidedtheprizes.Thankstotheworkshopchairs,whoprovided
the environment to put this workshop together.
June 2018 Zun Zhang
Lei Pan
Jonathan Oliver
BDM 2018 Workshop PC Chairs’ Message
For the past few years, biologically inspired data mining techniques have been
intensively used in different data mining applications such as data clustering, classi-
fication, association rule mining, sequential pattern mining, outlier detection, feature
selection and bioinformatics. The techniques include neural networks, evolutionary
computation, fuzzy systems, genetic algorithms, ant colony optimization, particle
swarmoptimization,artificialimmunesystem,culturealgorithms,socialevolution,and
artificialbeecolonyoptimization.Ahugeincreaseinthenumberofpaperspublishedin
theareahasbeenobservedinthelastdecade.Mostofthesetechniqueseitherhybridize
optimizationwithexistingdataminingtechniquestospeedupthedataminingprocess
oruse these techniques asindependent data mining methodstoimprovethequality of
patterns mined from the data.
The aim of the workshop is to highlight the current research related to biologically
inspired techniques in different data mining domains and their implementation in
real-life data mining problems. The workshop provides a platform to researchers from
computational intelligence and evolutionary computation and other biologically
inspired techniquestogetfeedback ontheirworkfromotherdata mining perspectives
such as statistical data mining, AI and machine learning based data mining.
Following the call for papers, BDM 2018 attracted 16 submissions from five
countries, and seven of them were accepted after a blind review by at least three
reviewers. The acceptance rate for the workshop was 40%.
Theselectedpapershighlightworkinmetaassociationrulesdiscoveryusingswarm
optimization, causal exploration in genome data, citation field learning using RNN,
Affine transformation capsule net, frequent itemsets mining in transactional databases,
rare events classification based on genetic algorithms, and PSO-based weighted
Nadaraya–Watson estimator.
We are thankful to all the authors who made this workshop possible by submitting
their work and responding positively to the changes suggested by our reviewers for
their work. Weare also thankfultoourProgram Committee, who dedicatedtheirtime
and provided us with their valuable suggestions and timely reviews. We wish to
expressourgratitudetotheworkshopchairs,whowerealwaysavailabletoanswerour
queries and provided us with everything we needed to put this workshop together.
June 2018 Shafiq Alam
Gillian Dobbie
Organization
Workshop of Big Data Analytics for Social Computing
(BDASC 2018)
General Chairs
Mark Western FASSA, Director of Institute for Social Science Research,
The University of Queensland, Australia
Junbin Gao Discipline of Business Analytics, The University
of Sydney Business School, The University of Sydney,
Australia
Program Chairs
Lin Wu Institute for Social Science Research, School
of Information Technology and Electrical Engineering,
The University of Queensland, Australia
Yang Wang Dalian University of Technology, China
Michele Haynes Learning Sciences Institute Australia, Australian Catholic
University, Brisbane Queensland, Australia
Program Committee
Meng Fang Tecent AI, China
Zengfeng Huang Fudan University, China
Liang Zheng University of Technology Sydney, Australia
Xiaojun Chang Carnegie Mellon University, USA
Qichang Hu The University of Adelaide, Australia
Zongyuan Ge IBM Research, Australia
Tong Chen The University of Queensland, Australia
Hongxu Chen The University of Queensland, Australia
Baichuan Zhang Facebook, USA
Xiang Zhao National University of Defence Technology, China
Hongcai Ma Chinese Academic of Science, China
Yifan Chen University of Amsterdam, The Netherlands
Xiaobo Shen Nanyang Technological University, Singapore
Xiaoyang Wang Zhejiang Gongshang University, China
Ying Zhang Dalian University of Technology, China
Xiaofeng Gong Dalian University of Technology, China
Wenda Zhao Dalian University of Technology, China
XII Organization
Chengyuan Zhang Central Southern University, China
Kim Betts The University of Queensland, Australia
Sponsorship: ARC Centre of Excellence for Children and Families
Over the Life Course
Pacific Asia Workshop on Intelligence and Security Informatics
(PAISI)
Workshop Co-chairs
Michael Chau The University of Hong Kong, SAR China
Hsinchun Chen The University of Arizona, USA
G. Alan Wang Virginia Tech, USA
Webmaster
Philip T. Y. Lee The University of Hong Kong, SAR China
Program Committee
Victor Benjamin Arizona State University, USA
Robert Weiping Chang Central Police University, Taiwan
Vladimir Estivill-Castro Griffith University, Australia
Uwe Gläesser Simon Fraser University, Canada
Daniel Hughes Massey University, Australia
Eul Gyu Im Hanyang University, South Korea
Da-Yu Kao Central Police University, Taiwan
Siddharth Kaza Towson University, USA
Wai Lam The Chinese University of Hong Kong, SAR China
Mark Last Ben-Gurion University of the Negev, Israel
Ickjai Lee James Cook University, Australia
Xin Li City University of Hong Kong, SAR China
You-Lu Liao Central Police University, Taiwan
Hsin-Min Lu National Taiwan University, Taiwan
Byron Marshall Oregon State University, USA
Dorbin Ng The Chinese University of Hong Kong, SAR China
Shaojie Qiao Southwest Jiaotong University, China