ROGUE BASE STATION ROUTER DETECTION WITH MACHINE LEARNING ALGORITHMS
This application is directed to a method for detecting a rogue device in a network. The method includes a step of surveying the network. The method also includes a step of collecting broadcast data from cellular towers in the network based on the survey. The method also includes a step of distilling...
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| creator | Stone, Kerri Ann Justin, Ronald Lance Ryan, Jennifer Lynn |
| description | This application is directed to a method for detecting a rogue device in a network. The method includes a step of surveying the network. The method also includes a step of collecting broadcast data from cellular towers in the network based on the survey. The method also includes a step of distilling the collected broadcast data into abstract syntax notation one (ASN.1)-encoded system information blocks (SIBs) associated with plural devices. The method further includes a step of featurizing the ASN.1-encoded SIBs. The method even further includes a step of running the featurized, ASN.1-encoded SIBs through an unsupervised machine learning algorithm. The algorithm is executed by a processor to analyze all cells in the survey for the rogue device. Yet even further, the method includes a step of determining, based on the run, anomalous cells exhibiting characteristics of the rogue device from all cells in the survey. |
| format | Patent |
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The method includes a step of surveying the network. The method also includes a step of collecting broadcast data from cellular towers in the network based on the survey. The method also includes a step of distilling the collected broadcast data into abstract syntax notation one (ASN.1)-encoded system information blocks (SIBs) associated with plural devices. The method further includes a step of featurizing the ASN.1-encoded SIBs. The method even further includes a step of running the featurized, ASN.1-encoded SIBs through an unsupervised machine learning algorithm. The algorithm is executed by a processor to analyze all cells in the survey for the rogue device. 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The method includes a step of surveying the network. The method also includes a step of collecting broadcast data from cellular towers in the network based on the survey. The method also includes a step of distilling the collected broadcast data into abstract syntax notation one (ASN.1)-encoded system information blocks (SIBs) associated with plural devices. The method further includes a step of featurizing the ASN.1-encoded SIBs. The method even further includes a step of running the featurized, ASN.1-encoded SIBs through an unsupervised machine learning algorithm. The algorithm is executed by a processor to analyze all cells in the survey for the rogue device. Yet even further, the method includes a step of determining, based on the run, anomalous cells exhibiting characteristics of the rogue device from all cells in the survey.</abstract><oa>free_for_read</oa></addata></record> |
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| 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 WIRELESS COMMUNICATIONS NETWORKS |
| title | ROGUE BASE STATION ROUTER DETECTION WITH MACHINE LEARNING ALGORITHMS |
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