Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and/or generating sanitized anomaly detection models


Ontology type: sgo:Patent     


Patent Info

DATE

N/A

AUTHORS

Gabriela CRETU , Angelos Stavrou , Salvatore J. Stolfo , Angelos D. Keromytis , Michael E. LOCASTO

ABSTRACT

Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and generating anomaly detection models are provided. In some embodiments, methods for generating sanitized data are provided. The methods including: dividing a first training dataset comprised of a plurality of training data items into a plurality of data subsets each including at least one training data item of the plurality of training data items of the first training dataset; based on the plurality of data subsets, generating a plurality of distinct anomaly detection micro-models; testing at least one data item of the plurality of data items of a second training dataset of training data items against each of the plurality of micro-models to produce a score for the at least one tested data item; and generating at least one output dataset based on the score for the at least one tested data item. More... »

Related SciGraph Publications

  • 1996-08. Bagging predictors in MACHINE LEARNING
  • 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/2008/2746", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "name": "Gabriela CRETU", 
            "type": "Person"
          }, 
          {
            "name": "Angelos Stavrou", 
            "type": "Person"
          }, 
          {
            "name": "Salvatore J. Stolfo", 
            "type": "Person"
          }, 
          {
            "name": "Angelos D. Keromytis", 
            "type": "Person"
          }, 
          {
            "name": "Michael E. LOCASTO", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/bf00058655", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002929950", 
              "https://doi.org/10.1007/bf00058655"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00058655", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002929950", 
              "https://doi.org/10.1007/bf00058655"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jcss.1997.1504", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004338842"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0893-6080(05)80023-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020902633"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/382912.382923", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052892569"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "description": "

    Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and generating anomaly detection models are provided. In some embodiments, methods for generating sanitized data are provided. The methods including: dividing a first training dataset comprised of a plurality of training data items into a plurality of data subsets each including at least one training data item of the plurality of training data items of the first training dataset; based on the plurality of data subsets, generating a plurality of distinct anomaly detection micro-models; testing at least one data item of the plurality of data items of a second training dataset of training data items against each of the plurality of micro-models to produce a score for the at least one tested data item; and generating at least one output dataset based on the score for the at least one tested data item.

    ", "id": "sg:patent.US-8407160-B2", "keywords": [ "method", "medium", "embodiment", "Dataset", "plurality", "training data", "subset", "micro", "data item", "score" ], "name": "Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and/or generating sanitized anomaly detection models", "recipient": [ { "id": "https://www.grid.ac/institutes/grid.21729.3f", "type": "Organization" } ], "sameAs": [ "https://app.dimensions.ai/details/patent/US-8407160-B2" ], "sdDataset": "patents", "sdDatePublished": "2019-03-07T15:31", "sdLicense": "https://scigraph.springernature.com/explorer/license/", "sdPublisher": { "name": "Springer Nature - SN SciGraph project", "type": "Organization" }, "sdSource": "s3://com.uberresearch.data.dev.patents-pipeline/full_run_10/sn-export/5eb3e5a348d7f117b22cc85fb0b02730/0000100128-0000348334/json_export_0db08f31.jsonl", "type": "Patent" } ]
     

    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/patent.US-8407160-B2'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/patent.US-8407160-B2'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/patent.US-8407160-B2'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/patent.US-8407160-B2'


     

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

    60 TRIPLES      14 PREDICATES      27 URIs      17 LITERALS      2 BLANK NODES

    Subject Predicate Object
    1 sg:patent.US-8407160-B2 schema:about anzsrc-for:2746
    2 schema:author N6c19982512b147cb86c885006d02d3c0
    3 schema:citation sg:pub.10.1007/bf00058655
    4 https://doi.org/10.1006/jcss.1997.1504
    5 https://doi.org/10.1016/s0893-6080(05)80023-1
    6 https://doi.org/10.1145/382912.382923
    7 schema:description <p num="p-0001">Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and generating anomaly detection models are provided. In some embodiments, methods for generating sanitized data are provided. The methods including: dividing a first training dataset comprised of a plurality of training data items into a plurality of data subsets each including at least one training data item of the plurality of training data items of the first training dataset; based on the plurality of data subsets, generating a plurality of distinct anomaly detection micro-models; testing at least one data item of the plurality of data items of a second training dataset of training data items against each of the plurality of micro-models to produce a score for the at least one tested data item; and generating at least one output dataset based on the score for the at least one tested data item.</p>
    8 schema:keywords Dataset
    9 data item
    10 embodiment
    11 medium
    12 method
    13 micro
    14 plurality
    15 score
    16 subset
    17 training data
    18 schema:name Systems, methods, and media for generating sanitized data, sanitizing anomaly detection models, and/or generating sanitized anomaly detection models
    19 schema:recipient https://www.grid.ac/institutes/grid.21729.3f
    20 schema:sameAs https://app.dimensions.ai/details/patent/US-8407160-B2
    21 schema:sdDatePublished 2019-03-07T15:31
    22 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    23 schema:sdPublisher N91ca1ce8686546babc6ed024c2f68b71
    24 sgo:license sg:explorer/license/
    25 sgo:sdDataset patents
    26 rdf:type sgo:Patent
    27 N3706bacffc5144c5a2a3e8cf232de655 rdf:first Neb744c9f251a4c04ad70dd9a7087c429
    28 rdf:rest rdf:nil
    29 N3e3b7813677e425c85c98032c17b13de rdf:first Nda2200a6064641eea7e8c24c7aaf2ba6
    30 rdf:rest Nf24573d849174f4087101b03a2fedf99
    31 N45eefd70580440288b96968a7a5ca13a schema:name Angelos D. Keromytis
    32 rdf:type schema:Person
    33 N6c19982512b147cb86c885006d02d3c0 rdf:first N8232f06540ef49b096174ac7804c85ae
    34 rdf:rest N3e3b7813677e425c85c98032c17b13de
    35 N7ea6f83f2f174237a78f41cd76afbb43 schema:name Salvatore J. Stolfo
    36 rdf:type schema:Person
    37 N8232f06540ef49b096174ac7804c85ae schema:name Gabriela CRETU
    38 rdf:type schema:Person
    39 N91ca1ce8686546babc6ed024c2f68b71 schema:name Springer Nature - SN SciGraph project
    40 rdf:type schema:Organization
    41 N96db5e645a9d41e882f12be6a3b38b7f rdf:first N45eefd70580440288b96968a7a5ca13a
    42 rdf:rest N3706bacffc5144c5a2a3e8cf232de655
    43 Nda2200a6064641eea7e8c24c7aaf2ba6 schema:name Angelos Stavrou
    44 rdf:type schema:Person
    45 Neb744c9f251a4c04ad70dd9a7087c429 schema:name Michael E. LOCASTO
    46 rdf:type schema:Person
    47 Nf24573d849174f4087101b03a2fedf99 rdf:first N7ea6f83f2f174237a78f41cd76afbb43
    48 rdf:rest N96db5e645a9d41e882f12be6a3b38b7f
    49 anzsrc-for:2746 schema:inDefinedTermSet anzsrc-for:
    50 rdf:type schema:DefinedTerm
    51 sg:pub.10.1007/bf00058655 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002929950
    52 https://doi.org/10.1007/bf00058655
    53 rdf:type schema:CreativeWork
    54 https://doi.org/10.1006/jcss.1997.1504 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004338842
    55 rdf:type schema:CreativeWork
    56 https://doi.org/10.1016/s0893-6080(05)80023-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020902633
    57 rdf:type schema:CreativeWork
    58 https://doi.org/10.1145/382912.382923 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052892569
    59 rdf:type schema:CreativeWork
    60 https://www.grid.ac/institutes/grid.21729.3f schema:Organization
     




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


    ...