SYSTEMS AND METHODS FOR DATA-DRIVEN INFRASTRUCTURE CONTROLS
Systems and methods for data-driven infrastructure controls are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, a computer-implemented method for automatically detecting anomalous user behavior within a unified entitlement fr...
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| creator | BELINKIS, Vladimir CHAN, Ricky Hei Wong AHLUWALIA, Ish K HETTIARACHCHI, Kanishka JOSEPH, Dennis KURUP, Rejith G |
| description | Systems and methods for data-driven infrastructure controls are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, a computer-implemented method for automatically detecting anomalous user behavior within a unified entitlement framework may include: (1) receiving an access request for a technology asset from a user on a computing device, the access request comprising session data comprising one or more of user identification, user location, key strokes, and user computing device identification; (2) applying an entitlement-specific machine learning algorithm to the session data to generate an anomaly score; (3) storing the session data and associated anomaly score; (4) sending a review request to a manager; (5) receiving review results from the manager; and (6) updating the entitlement-specific machine learning algorithm based on the anomaly score and the review results from the manager. |
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According to one embodiment, in an information processing apparatus comprising at least one computer processor, a computer-implemented method for automatically detecting anomalous user behavior within a unified entitlement framework may include: (1) receiving an access request for a technology asset from a user on a computing device, the access request comprising session data comprising one or more of user identification, user location, key strokes, and user computing device identification; (2) applying an entitlement-specific machine learning algorithm to the session data to generate an anomaly score; (3) storing the session data and associated anomaly score; (4) sending a review request to a manager; (5) receiving review results from the manager; and (6) updating the entitlement-specific machine learning algorithm based on the anomaly score and the review results from the manager.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC COMMUNICATION TECHNIQUE ; ELECTRIC DIGITAL DATA PROCESSING ; ELECTRICITY ; PHYSICS ; 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=20201029&DB=EPODOC&CC=US&NR=2020344253A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201029&DB=EPODOC&CC=US&NR=2020344253A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>BELINKIS, Vladimir</creatorcontrib><creatorcontrib>CHAN, Ricky Hei Wong</creatorcontrib><creatorcontrib>AHLUWALIA, Ish K</creatorcontrib><creatorcontrib>HETTIARACHCHI, Kanishka</creatorcontrib><creatorcontrib>JOSEPH, Dennis</creatorcontrib><creatorcontrib>KURUP, Rejith G</creatorcontrib><title>SYSTEMS AND METHODS FOR DATA-DRIVEN INFRASTRUCTURE CONTROLS</title><description>Systems and methods for data-driven infrastructure controls are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, a computer-implemented method for automatically detecting anomalous user behavior within a unified entitlement framework may include: (1) receiving an access request for a technology asset from a user on a computing device, the access request comprising session data comprising one or more of user identification, user location, key strokes, and user computing device identification; (2) applying an entitlement-specific machine learning algorithm to the session data to generate an anomaly score; (3) storing the session data and associated anomaly score; (4) sending a review request to a manager; (5) receiving review results from the manager; and (6) updating the entitlement-specific machine learning algorithm based on the anomaly score and the review results from the manager.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC COMMUNICATION TECHNIQUE</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>ELECTRICITY</subject><subject>PHYSICS</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>eNrjZLAOjgwOcfUNVnD0c1HwdQ3x8HcJVnDzD1JwcQxx1HUJ8gxz9VPw9HMLcgwOCQp1DgkNclVw9vcLCfL3CeZhYE1LzClO5YXS3AzKbq4hzh66qQX58anFBYnJqXmpJfGhwUYGRgbGJiZGpsaOhsbEqQIAqusqkg</recordid><startdate>20201029</startdate><enddate>20201029</enddate><creator>BELINKIS, Vladimir</creator><creator>CHAN, Ricky Hei Wong</creator><creator>AHLUWALIA, Ish K</creator><creator>HETTIARACHCHI, Kanishka</creator><creator>JOSEPH, Dennis</creator><creator>KURUP, Rejith G</creator><scope>EVB</scope></search><sort><creationdate>20201029</creationdate><title>SYSTEMS AND METHODS FOR DATA-DRIVEN INFRASTRUCTURE CONTROLS</title><author>BELINKIS, Vladimir ; CHAN, Ricky Hei Wong ; AHLUWALIA, Ish K ; HETTIARACHCHI, Kanishka ; JOSEPH, Dennis ; KURUP, Rejith G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2020344253A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC COMMUNICATION TECHNIQUE</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>ELECTRICITY</topic><topic>PHYSICS</topic><topic>TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION</topic><toplevel>online_resources</toplevel><creatorcontrib>BELINKIS, Vladimir</creatorcontrib><creatorcontrib>CHAN, Ricky Hei Wong</creatorcontrib><creatorcontrib>AHLUWALIA, Ish K</creatorcontrib><creatorcontrib>HETTIARACHCHI, Kanishka</creatorcontrib><creatorcontrib>JOSEPH, Dennis</creatorcontrib><creatorcontrib>KURUP, Rejith G</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>BELINKIS, Vladimir</au><au>CHAN, Ricky Hei Wong</au><au>AHLUWALIA, Ish K</au><au>HETTIARACHCHI, Kanishka</au><au>JOSEPH, Dennis</au><au>KURUP, Rejith G</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>SYSTEMS AND METHODS FOR DATA-DRIVEN INFRASTRUCTURE CONTROLS</title><date>2020-10-29</date><risdate>2020</risdate><abstract>Systems and methods for data-driven infrastructure controls are disclosed. According to one embodiment, in an information processing apparatus comprising at least one computer processor, a computer-implemented method for automatically detecting anomalous user behavior within a unified entitlement framework may include: (1) receiving an access request for a technology asset from a user on a computing device, the access request comprising session data comprising one or more of user identification, user location, key strokes, and user computing device identification; (2) applying an entitlement-specific machine learning algorithm to the session data to generate an anomaly score; (3) storing the session data and associated anomaly score; (4) sending a review request to a manager; (5) receiving review results from the manager; and (6) updating the entitlement-specific machine learning algorithm based on the anomaly score and the review results from the manager.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRIC DIGITAL DATA PROCESSING ELECTRICITY PHYSICS TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION |
| title | SYSTEMS AND METHODS FOR DATA-DRIVEN INFRASTRUCTURE CONTROLS |
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