DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-12-28

AUTHORS

Yubin Yang, Zongtao Wei, Yong Xu, Haiwu He, Wei Wang

ABSTRACT

As the number of Android malicious applications has explosively increased, effectively vetting Android applications (apps) has become an emerging issue. Traditional static analysis is ineffective for vetting apps whose code have been obfuscated or encrypted. Dynamic analysis is suitable to deal with the obfuscation and encryption of codes. However, existing dynamic analysis methods cannot effectively vet the applications, as a limited number of dynamic features have been explored from apps that have become increasingly sophisticated. In this work, we propose an effective dynamic analysis method called DroidWard in the aim to extract most relevant and effective features to characterize malicious behavior and to improve the detection accuracy of malicious apps. In addition to using the existing 9 features, DroidWard extracts 6 novel types of effective features from apps through dynamic analysis. DroidWard runs apps, extracts features and identifies benign and malicious apps with Support Vector Machine (SVM), Decision Tree (DTree) and Random Forest. 666 Android apps are used in the experiments and the evaluation results show that DroidWard correctly classifies 98.54% of malicious apps with 1.55% of false positives. Compared to existing work, DroidWard improves the TPR with 16.07% and suppresses the FPR with 1.31% with SVM, indicating that it is more effective than existing methods. More... »

PAGES

1-11

References to SciGraph publications

  • 2017. Alde: Privacy Risk Analysis of Analytics Libraries in the Android Ecosystem in SECURITY AND PRIVACY IN COMMUNICATION NETWORKS
  • 2001-10. Random Forests in MACHINE LEARNING
  • 1998-06. A Tutorial on Support Vector Machines for Pattern Recognition in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2013-07. Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering in JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
  • 2012-05. Reducing the window of opportunity for Android malware Gotta catch ’em all in JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES
  • 2015-02. Identifying Android malware using dynamically obtained features in JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10586-016-0703-5

    DOI

    http://dx.doi.org/10.1007/s10586-016-0703-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1037205919


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "South China University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.79703.3a", 
              "name": [
                "School of Computer Science & Engineering, South China University of Technology, 510641, Guangzhou, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yang", 
            "givenName": "Yubin", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Beijing Jiaotong University", 
              "id": "https://www.grid.ac/institutes/grid.181531.f", 
              "name": [
                "School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wei", 
            "givenName": "Zongtao", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "South China University of Technology", 
              "id": "https://www.grid.ac/institutes/grid.79703.3a", 
              "name": [
                "School of Computer Science & Engineering, South China University of Technology, 510641, Guangzhou, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xu", 
            "givenName": "Yong", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Computer Network Information Center", 
              "id": "https://www.grid.ac/institutes/grid.433146.7", 
              "name": [
                "Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "He", 
            "givenName": "Haiwu", 
            "id": "sg:person.011053257402.21", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011053257402.21"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Beijing Jiaotong University", 
              "id": "https://www.grid.ac/institutes/grid.181531.f", 
              "name": [
                "School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Wei", 
            "id": "sg:person.011375705417.33", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011375705417.33"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.ins.2016.10.023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005410143"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jnca.2008.04.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010091310"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2557547.2557563", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013039686"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2014.06.018", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015119716"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11416-014-0226-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019956480", 
              "https://doi.org/10.1007/s11416-014-0226-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2046707.2046779", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023848925"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11416-012-0162-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023857859", 
              "https://doi.org/10.1007/s11416-012-0162-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4236/jis.2013.44024", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024479276"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1010933404324", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024739340", 
              "https://doi.org/10.1023/a:1010933404324"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.comcom.2007.10.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027441616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11390-013-1362-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032546386", 
              "https://doi.org/10.1007/s11390-013-1362-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2480362.2480701", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033547244"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jss.2009.06.040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034026230"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1009715923555", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042048349", 
              "https://doi.org/10.1023/a:1009715923555"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2619091", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045499326"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cose.2006.05.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047442097"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1049/iet-ifs.2014.0353", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056828706"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tc.2013.208", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061535589"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tifs.2014.2353996", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061630375"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tkde.2013.146", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061662730"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpds.2013.271", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061754408"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpds.2013.284", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061754421"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpds.2014.2318320", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061754616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-59608-2_36", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086027952", 
              "https://doi.org/10.1007/978-3-319-59608-2_36"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icimp.2008.13", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093571130"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/badgers.2014.7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093966735"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/sp.2012.16", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094209237"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ares.2006.73", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094736636"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/pccc.2015.7410335", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094803761"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/trustcom.2016.0127", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095019929"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/compsac.2015.103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095251744"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ictai.2013.53", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095550402"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.14722/ndss.2014.23247", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095872940"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2016-12-28", 
        "datePublishedReg": "2016-12-28", 
        "description": "As the number of Android malicious applications has explosively increased, effectively vetting Android applications (apps) has become an emerging issue. Traditional static analysis is ineffective for vetting apps whose code have been obfuscated or encrypted. Dynamic analysis is suitable to deal with the obfuscation and encryption of codes. However, existing dynamic analysis methods cannot effectively vet the applications, as a limited number of dynamic features have been explored from apps that have become increasingly sophisticated. In this work, we propose an effective dynamic analysis method called DroidWard in the aim to extract most relevant and effective features to characterize malicious behavior and to improve the detection accuracy of malicious apps. In addition to using the existing 9 features, DroidWard extracts 6 novel types of effective features from apps through dynamic analysis. DroidWard runs apps, extracts features and identifies benign and malicious apps with Support Vector Machine (SVM), Decision Tree (DTree) and Random Forest. 666 Android apps are used in the experiments and the evaluation results show that DroidWard correctly classifies 98.54% of malicious apps with 1.55% of false positives. Compared to existing work, DroidWard improves the TPR with 16.07% and suppresses the FPR with 1.31% with SVM, indicating that it is more effective than existing methods.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10586-016-0703-5", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1046649", 
            "issn": [
              "1386-7857", 
              "1573-7543"
            ], 
            "name": "Cluster Computing", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "21"
          }
        ], 
        "name": "DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications", 
        "pagination": "1-11", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "b13c3c7394f572508cb7160d2e1378019b83c0794400fcfd9b9c9be5e2bed1d9"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10586-016-0703-5"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1037205919"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10586-016-0703-5", 
          "https://app.dimensions.ai/details/publication/pub.1037205919"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T19:52", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8681_00000490.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/s10586-016-0703-5"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10586-016-0703-5'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10586-016-0703-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10586-016-0703-5'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10586-016-0703-5'


     

    This table displays all metadata directly associated to this object as RDF triples.

    197 TRIPLES      21 PREDICATES      59 URIs      18 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10586-016-0703-5 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nf0eab0d3b1d343bcaa55762745876224
    4 schema:citation sg:pub.10.1007/978-3-319-59608-2_36
    5 sg:pub.10.1007/s11390-013-1362-0
    6 sg:pub.10.1007/s11416-012-0162-3
    7 sg:pub.10.1007/s11416-014-0226-7
    8 sg:pub.10.1023/a:1009715923555
    9 sg:pub.10.1023/a:1010933404324
    10 https://doi.org/10.1016/j.comcom.2007.10.010
    11 https://doi.org/10.1016/j.cose.2006.05.005
    12 https://doi.org/10.1016/j.ins.2016.10.023
    13 https://doi.org/10.1016/j.jnca.2008.04.006
    14 https://doi.org/10.1016/j.jss.2009.06.040
    15 https://doi.org/10.1016/j.knosys.2014.06.018
    16 https://doi.org/10.1049/iet-ifs.2014.0353
    17 https://doi.org/10.1109/ares.2006.73
    18 https://doi.org/10.1109/badgers.2014.7
    19 https://doi.org/10.1109/compsac.2015.103
    20 https://doi.org/10.1109/icimp.2008.13
    21 https://doi.org/10.1109/ictai.2013.53
    22 https://doi.org/10.1109/pccc.2015.7410335
    23 https://doi.org/10.1109/sp.2012.16
    24 https://doi.org/10.1109/tc.2013.208
    25 https://doi.org/10.1109/tifs.2014.2353996
    26 https://doi.org/10.1109/tkde.2013.146
    27 https://doi.org/10.1109/tpds.2013.271
    28 https://doi.org/10.1109/tpds.2013.284
    29 https://doi.org/10.1109/tpds.2014.2318320
    30 https://doi.org/10.1109/trustcom.2016.0127
    31 https://doi.org/10.1145/2046707.2046779
    32 https://doi.org/10.1145/2480362.2480701
    33 https://doi.org/10.1145/2557547.2557563
    34 https://doi.org/10.1145/2619091
    35 https://doi.org/10.14722/ndss.2014.23247
    36 https://doi.org/10.4236/jis.2013.44024
    37 schema:datePublished 2016-12-28
    38 schema:datePublishedReg 2016-12-28
    39 schema:description As the number of Android malicious applications has explosively increased, effectively vetting Android applications (apps) has become an emerging issue. Traditional static analysis is ineffective for vetting apps whose code have been obfuscated or encrypted. Dynamic analysis is suitable to deal with the obfuscation and encryption of codes. However, existing dynamic analysis methods cannot effectively vet the applications, as a limited number of dynamic features have been explored from apps that have become increasingly sophisticated. In this work, we propose an effective dynamic analysis method called DroidWard in the aim to extract most relevant and effective features to characterize malicious behavior and to improve the detection accuracy of malicious apps. In addition to using the existing 9 features, DroidWard extracts 6 novel types of effective features from apps through dynamic analysis. DroidWard runs apps, extracts features and identifies benign and malicious apps with Support Vector Machine (SVM), Decision Tree (DTree) and Random Forest. 666 Android apps are used in the experiments and the evaluation results show that DroidWard correctly classifies 98.54% of malicious apps with 1.55% of false positives. Compared to existing work, DroidWard improves the TPR with 16.07% and suppresses the FPR with 1.31% with SVM, indicating that it is more effective than existing methods.
    40 schema:genre research_article
    41 schema:inLanguage en
    42 schema:isAccessibleForFree false
    43 schema:isPartOf N6ab9a75e129546d9b1fbf08aceaf5ae3
    44 Na27600b84c444f518d9e028f268c4067
    45 sg:journal.1046649
    46 schema:name DroidWard: An Effective Dynamic Analysis Method for Vetting Android Applications
    47 schema:pagination 1-11
    48 schema:productId N02072c081ab94d9fa9ebb48d010a7a48
    49 N18523023178c42b08f290a2a2f710d34
    50 Ncf2cc64a17bc4c8ba79ae6897e55764b
    51 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037205919
    52 https://doi.org/10.1007/s10586-016-0703-5
    53 schema:sdDatePublished 2019-04-10T19:52
    54 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    55 schema:sdPublisher Nd63f2e2921694c4bb1c1a4b186f7ea03
    56 schema:url http://link.springer.com/10.1007/s10586-016-0703-5
    57 sgo:license sg:explorer/license/
    58 sgo:sdDataset articles
    59 rdf:type schema:ScholarlyArticle
    60 N02072c081ab94d9fa9ebb48d010a7a48 schema:name dimensions_id
    61 schema:value pub.1037205919
    62 rdf:type schema:PropertyValue
    63 N1416b07c83fa48bc82b5639742e1b9b9 schema:affiliation https://www.grid.ac/institutes/grid.79703.3a
    64 schema:familyName Xu
    65 schema:givenName Yong
    66 rdf:type schema:Person
    67 N18523023178c42b08f290a2a2f710d34 schema:name readcube_id
    68 schema:value b13c3c7394f572508cb7160d2e1378019b83c0794400fcfd9b9c9be5e2bed1d9
    69 rdf:type schema:PropertyValue
    70 N2de04524a9f74b4ba8a53d63eb01d018 rdf:first N1416b07c83fa48bc82b5639742e1b9b9
    71 rdf:rest N7df888aa75ca4ab3ac475dcf6ea30355
    72 N6ab9a75e129546d9b1fbf08aceaf5ae3 schema:volumeNumber 21
    73 rdf:type schema:PublicationVolume
    74 N7df888aa75ca4ab3ac475dcf6ea30355 rdf:first sg:person.011053257402.21
    75 rdf:rest Nced0052fc8a44ea6b083cb202affb966
    76 N961702e894034e06aa064b8adc2a57c8 schema:affiliation https://www.grid.ac/institutes/grid.79703.3a
    77 schema:familyName Yang
    78 schema:givenName Yubin
    79 rdf:type schema:Person
    80 Na27600b84c444f518d9e028f268c4067 schema:issueNumber 1
    81 rdf:type schema:PublicationIssue
    82 Ncd6fcbabff444592a36b6fdc5e792f8c rdf:first Nd9a078fb336742939f9846f3c70ffca2
    83 rdf:rest N2de04524a9f74b4ba8a53d63eb01d018
    84 Nced0052fc8a44ea6b083cb202affb966 rdf:first sg:person.011375705417.33
    85 rdf:rest rdf:nil
    86 Ncf2cc64a17bc4c8ba79ae6897e55764b schema:name doi
    87 schema:value 10.1007/s10586-016-0703-5
    88 rdf:type schema:PropertyValue
    89 Nd63f2e2921694c4bb1c1a4b186f7ea03 schema:name Springer Nature - SN SciGraph project
    90 rdf:type schema:Organization
    91 Nd9a078fb336742939f9846f3c70ffca2 schema:affiliation https://www.grid.ac/institutes/grid.181531.f
    92 schema:familyName Wei
    93 schema:givenName Zongtao
    94 rdf:type schema:Person
    95 Nf0eab0d3b1d343bcaa55762745876224 rdf:first N961702e894034e06aa064b8adc2a57c8
    96 rdf:rest Ncd6fcbabff444592a36b6fdc5e792f8c
    97 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Information and Computing Sciences
    99 rdf:type schema:DefinedTerm
    100 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Artificial Intelligence and Image Processing
    102 rdf:type schema:DefinedTerm
    103 sg:journal.1046649 schema:issn 1386-7857
    104 1573-7543
    105 schema:name Cluster Computing
    106 rdf:type schema:Periodical
    107 sg:person.011053257402.21 schema:affiliation https://www.grid.ac/institutes/grid.433146.7
    108 schema:familyName He
    109 schema:givenName Haiwu
    110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011053257402.21
    111 rdf:type schema:Person
    112 sg:person.011375705417.33 schema:affiliation https://www.grid.ac/institutes/grid.181531.f
    113 schema:familyName Wang
    114 schema:givenName Wei
    115 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011375705417.33
    116 rdf:type schema:Person
    117 sg:pub.10.1007/978-3-319-59608-2_36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086027952
    118 https://doi.org/10.1007/978-3-319-59608-2_36
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/s11390-013-1362-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032546386
    121 https://doi.org/10.1007/s11390-013-1362-0
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/s11416-012-0162-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023857859
    124 https://doi.org/10.1007/s11416-012-0162-3
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1007/s11416-014-0226-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019956480
    127 https://doi.org/10.1007/s11416-014-0226-7
    128 rdf:type schema:CreativeWork
    129 sg:pub.10.1023/a:1009715923555 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042048349
    130 https://doi.org/10.1023/a:1009715923555
    131 rdf:type schema:CreativeWork
    132 sg:pub.10.1023/a:1010933404324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024739340
    133 https://doi.org/10.1023/a:1010933404324
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1016/j.comcom.2007.10.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027441616
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1016/j.cose.2006.05.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047442097
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1016/j.ins.2016.10.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005410143
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1016/j.jnca.2008.04.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010091310
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1016/j.jss.2009.06.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034026230
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1016/j.knosys.2014.06.018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015119716
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1049/iet-ifs.2014.0353 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056828706
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1109/ares.2006.73 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094736636
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1109/badgers.2014.7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093966735
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1109/compsac.2015.103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095251744
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1109/icimp.2008.13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093571130
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1109/ictai.2013.53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095550402
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1109/pccc.2015.7410335 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094803761
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1109/sp.2012.16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094209237
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1109/tc.2013.208 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061535589
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1109/tifs.2014.2353996 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061630375
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1109/tkde.2013.146 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061662730
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1109/tpds.2013.271 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061754408
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1109/tpds.2013.284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061754421
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1109/tpds.2014.2318320 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061754616
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1109/trustcom.2016.0127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095019929
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1145/2046707.2046779 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023848925
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1145/2480362.2480701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033547244
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1145/2557547.2557563 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013039686
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1145/2619091 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045499326
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.14722/ndss.2014.23247 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095872940
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.4236/jis.2013.44024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024479276
    188 rdf:type schema:CreativeWork
    189 https://www.grid.ac/institutes/grid.181531.f schema:alternateName Beijing Jiaotong University
    190 schema:name School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China
    191 rdf:type schema:Organization
    192 https://www.grid.ac/institutes/grid.433146.7 schema:alternateName Computer Network Information Center
    193 schema:name Computer Network Information Center, Chinese Academy of Sciences, 100190, Beijing, China
    194 rdf:type schema:Organization
    195 https://www.grid.ac/institutes/grid.79703.3a schema:alternateName South China University of Technology
    196 schema:name School of Computer Science & Engineering, South China University of Technology, 510641, Guangzhou, China
    197 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...