MALICIOUS SOFTWARE RECOGNITION APPARATUS AND METHOD
A malicious software recognition apparatus and method are provided. The malicious software recognition apparatus stores a training dataset, which includes a plurality of network flow datasets. Each network flow dataset corresponds to one of a plurality of software categories, and the software catego...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Patent |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | HSU, Wei-Chao CHEN, Jiann-Liang CHEN, Yu-Hung CHEN, Yan-Ju KE, Ying-Tsun |
| description | A malicious software recognition apparatus and method are provided. The malicious software recognition apparatus stores a training dataset, which includes a plurality of network flow datasets. Each network flow dataset corresponds to one of a plurality of software categories, and the software categories include a plurality of malicious software categories. The malicious software recognition apparatus tests a malicious software recognition model and learns that a plurality of recognition accuracies of a subset of the malicious software categories are low, determines that an overlap degree of the network flow datasets corresponding to the subset is high, updates the software categories by combining the malicious software categories corresponding to the subset, updates the training dataset by integrating the network flow datasets corresponding to the subset, trains the malicious software recognition model according to the updated training dataset. The trained malicious software recognition model is deployed to the real world. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2020125896A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2020125896A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2020125896A13</originalsourceid><addsrcrecordid>eNrjZDD2dfTxdPb0Dw1WCPZ3Cwl3DHJVCHJ19nf38wzx9PdTcAwIcAxyDAFKO_q5KPi6hnj4u_AwsKYl5hSn8kJpbgZlN9cQZw_d1IL8-NTigsTk1LzUkvjQYCMDIwNDI1MLSzNHQ2PiVAEAXAAoSw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>MALICIOUS SOFTWARE RECOGNITION APPARATUS AND METHOD</title><source>esp@cenet</source><creator>HSU, Wei-Chao ; CHEN, Jiann-Liang ; CHEN, Yu-Hung ; CHEN, Yan-Ju ; KE, Ying-Tsun</creator><creatorcontrib>HSU, Wei-Chao ; CHEN, Jiann-Liang ; CHEN, Yu-Hung ; CHEN, Yan-Ju ; KE, Ying-Tsun</creatorcontrib><description>A malicious software recognition apparatus and method are provided. The malicious software recognition apparatus stores a training dataset, which includes a plurality of network flow datasets. Each network flow dataset corresponds to one of a plurality of software categories, and the software categories include a plurality of malicious software categories. The malicious software recognition apparatus tests a malicious software recognition model and learns that a plurality of recognition accuracies of a subset of the malicious software categories are low, determines that an overlap degree of the network flow datasets corresponding to the subset is high, updates the software categories by combining the malicious software categories corresponding to the subset, updates the training dataset by integrating the network flow datasets corresponding to the subset, trains the malicious software recognition model according to the updated training dataset. The trained malicious software recognition model is deployed to the real world.</description><language>eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRIC DIGITAL DATA PROCESSING ; ELECTRICITY ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS ; TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200423&DB=EPODOC&CC=US&NR=2020125896A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20200423&DB=EPODOC&CC=US&NR=2020125896A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HSU, Wei-Chao</creatorcontrib><creatorcontrib>CHEN, Jiann-Liang</creatorcontrib><creatorcontrib>CHEN, Yu-Hung</creatorcontrib><creatorcontrib>CHEN, Yan-Ju</creatorcontrib><creatorcontrib>KE, Ying-Tsun</creatorcontrib><title>MALICIOUS SOFTWARE RECOGNITION APPARATUS AND METHOD</title><description>A malicious software recognition apparatus and method are provided. The malicious software recognition apparatus stores a training dataset, which includes a plurality of network flow datasets. Each network flow dataset corresponds to one of a plurality of software categories, and the software categories include a plurality of malicious software categories. The malicious software recognition apparatus tests a malicious software recognition model and learns that a plurality of recognition accuracies of a subset of the malicious software categories are low, determines that an overlap degree of the network flow datasets corresponding to the subset is high, updates the software categories by combining the malicious software categories corresponding to the subset, updates the training dataset by integrating the network flow datasets corresponding to the subset, trains the malicious software recognition model according to the updated training dataset. The trained malicious software recognition model is deployed to the real world.</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>ELECTRICITY</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><subject>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDD2dfTxdPb0Dw1WCPZ3Cwl3DHJVCHJ19nf38wzx9PdTcAwIcAxyDAFKO_q5KPi6hnj4u_AwsKYl5hSn8kJpbgZlN9cQZw_d1IL8-NTigsTk1LzUkvjQYCMDIwNDI1MLSzNHQ2PiVAEAXAAoSw</recordid><startdate>20200423</startdate><enddate>20200423</enddate><creator>HSU, Wei-Chao</creator><creator>CHEN, Jiann-Liang</creator><creator>CHEN, Yu-Hung</creator><creator>CHEN, Yan-Ju</creator><creator>KE, Ying-Tsun</creator><scope>EVB</scope></search><sort><creationdate>20200423</creationdate><title>MALICIOUS SOFTWARE RECOGNITION APPARATUS AND METHOD</title><author>HSU, Wei-Chao ; CHEN, Jiann-Liang ; CHEN, Yu-Hung ; CHEN, Yan-Ju ; KE, Ying-Tsun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2020125896A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>ELECTRICITY</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><topic>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</topic><toplevel>online_resources</toplevel><creatorcontrib>HSU, Wei-Chao</creatorcontrib><creatorcontrib>CHEN, Jiann-Liang</creatorcontrib><creatorcontrib>CHEN, Yu-Hung</creatorcontrib><creatorcontrib>CHEN, Yan-Ju</creatorcontrib><creatorcontrib>KE, Ying-Tsun</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HSU, Wei-Chao</au><au>CHEN, Jiann-Liang</au><au>CHEN, Yu-Hung</au><au>CHEN, Yan-Ju</au><au>KE, Ying-Tsun</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MALICIOUS SOFTWARE RECOGNITION APPARATUS AND METHOD</title><date>2020-04-23</date><risdate>2020</risdate><abstract>A malicious software recognition apparatus and method are provided. The malicious software recognition apparatus stores a training dataset, which includes a plurality of network flow datasets. Each network flow dataset corresponds to one of a plurality of software categories, and the software categories include a plurality of malicious software categories. The malicious software recognition apparatus tests a malicious software recognition model and learns that a plurality of recognition accuracies of a subset of the malicious software categories are low, determines that an overlap degree of the network flow datasets corresponding to the subset is high, updates the software categories by combining the malicious software categories corresponding to the subset, updates the training dataset by integrating the network flow datasets corresponding to the subset, trains the malicious software recognition model according to the updated training dataset. The trained malicious software recognition model is deployed to the real world.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_US2020125896A1 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRIC DIGITAL DATA PROCESSING ELECTRICITY HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION |
| title | MALICIOUS SOFTWARE RECOGNITION APPARATUS AND METHOD |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T19%3A29%3A50IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=HSU,%20Wei-Chao&rft.date=2020-04-23&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2020125896A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |