Mohadeseh Ganji · Lida Rashidi Benjamin C. M. Fung · Can Wang (Eds.) Trends and Applications 4 5 1 in Knowledge Discovery 1 1 I A and Data Mining N L PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO Melbourne, VIC, Australia, June 3, 2018 Revised Selected Papers 123 fi 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 LibraryofCongressControlNumber:2018961439 LNCSSublibrary:SL7–ArtificialIntelligence ©SpringerNatureSwitzerlandAG2018 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthors,andtheeditorsaresafetoassumethattheadviceandinformationinthisbookare believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictionalclaimsin publishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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