Abstract: A thermoplastic film includes a core layer having a recycled polyethylene terephthalate (PET) based resin having an intrinsic viscosity in the range of about 0.579 to about 0.70. The thermoplastic film comprises about 90% to about 100% by weight of the recycled PET resin.

Abstract: Systems and methods of authenticating voice data using a ledger (blockchain). Examples include a scalable and seamless system that uses blockchain technologies to distribute trust of a conversation, authenticate persons in a conversation, track their characteristics and also to keep records of conversations. In some examples, smart phones, wearables, and Internet-of-Things (IoT) devices can be used to record and track conversations between individuals. These devices can each be used to create entries for the blockchain or a single device could be used to keep track of the entirety of the conversation. Fuzzy hashing may be used to compare newly created entries with previous entries on the ledger.

Abstract: Techniques are disclosed relating to generating trained machine learning modules to identify whether user interfaces accessed by a computing device match user interfaces associated with a set of Internet domain names. A server computer system receives a set of Internet domain names and generates screenshots for user interfaces associated with the set of Internet domain names. The server computer system then trains machine learning modules that are customized for the set of Internet domain names using the screenshots. The server then transmits the machine learning modules to the computing device, where the machine learning modules are usable by an application executing on the computing device to identify whether a user interface accessed by the device matches a user interface associated with the set of Internet domain names. Such techniques may advantageously allow servers to identify whether user interfaces are suspicious without introducing latency and increased page load times.

Abstract: Methods and systems for detecting webpages that share malicious content are presented. A first set of webpages that hosts a web account checker is identified. A baseline page structure score and a baseline language score are calculated based on the identified first set of webpages. Content from a second set of webpages is collected and analyzed based on the calculated baseline page structure and the calculated baseline language scores. One or more of the second set of webpages is flagged as malicious based on the analyzing of the content collected from the second set of webpages.

Abstract: There are provided systems and methods for a voice vector framework that authenticates user interactions. A service provider server receives user interaction data having audio data that is associated with an interaction between a user device and the service provider server. The server extracts user attributes from the audio data and obtains user account information associated with the user device. The server selects a classifier that corresponds to a select combination of features based on the user account information and applies the classifier to the user attributes. The server generates a voice vector that includes multiple scores indicating likelihoods that a respective user attribute corresponds to an attribute of the select combination of features. The server compares the voice vector to a baseline vector corresponding to a predetermined combination of features and sends a notification to an agent device with an indication of whether the user device is verified.

Abstract: Methods and systems are presented for dynamically adjusting a risk classification of a risk source based on classifications of one or more other risk sources. The risk engine may first classify a first risk source as a first risk type based on an initial analysis of the first risk source. Subsequent to classifying the first risk source as the first risk type, the risk engine may determine that a second risk source is associated with a second risk type. Based on the determination that the second risk source is associated with the second risk type, the risk engine may re-classify the first risk source as the second risk type. The risk engine may then use the reclassification of the first risk source to improve network security of an online service provider.

Abstract: A method for phishing detection using uniform resource locators is discussed. The method includes accessing data from one or more of a monitored portion of website data and a monitored portion of emails, the data indicating a suspect Uniform Resource Locator (URL). The method includes assigning a rule score based on partial rule scores of each portion of the suspect URL, the rule score indicating a phishing potential based on URL rules. The method includes determining a uniqueness score of the suspect URL, the uniqueness score indicating a degree of uniqueness of the suspect URL from a plurality of known phishing URLs. The method also includes determining a URL phishing score based, at least in part, on the rules scores and the uniqueness score for the suspect URL.

Abstract: Techniques are disclosed relating to generating trained machine learning modules to identify whether user interfaces accessed by a computing device match user interfaces associated with a set of Internet domain names. A server computer system receives a set of Internet domain names and generates screenshots for user interfaces associated with the set of Internet domain names. The server computer system then trains machine learning modules that are customized for the set of Internet domain names using the screenshots. The server then transmits the machine learning modules to the computing device, where the machine learning modules are usable by an application executing on the computing device to identify whether a user interface accessed by the device matches a user interface associated with the set of Internet domain names. Such techniques may advantageously allow servers to identify whether user interfaces are suspicious without introducing latency and increased page load times.

Abstract: There are provided systems and methods for actionable insight into user interaction data. A service provider server can access user interaction data associated with an interaction between a first communication device and the service provider server, and generates feature representations of the user interaction data, in which the feature representations respectively correspond to extracted features that include textual data features or audio data features. The service provider server can determine an intent of the interaction from the feature representations using a machine learning-trained classifier, in which the intent corresponds to a first actionable insight category. The interaction is mapped to a first cluster based on the intent, and the service provider server issues a remedial action for the interaction based on the mapping of the interaction to the first cluster, in which the remedial action is associated with a particular type of activity in the first actionable insight category.

Abstract: Systems and methods of authenticating voice data using a ledger (blockchain). Examples include a scalable and seamless system that uses blockchain technologies to distribute trust of a conversation, authenticate persons in a conversation, track their characteristics and also to keep records of conversations. In some examples, smart phones, wearables, and Internet-of-Things (IoT) devices can be used to record and track conversations between individuals. These devices can each be used to create entries for the blockchain or a single device could be used to keep track of the entirety of the conversation. Fuzzy hashing may be used to compare newly created entries with previous entries on the ledger.

Abstract: Methods and systems for assessing and evaluating vulnerabilities of a networked system are presented. A list of known vulnerabilities that have been disclosed in the public may be obtained. The networked system may be scanned from an external perspective to obtain network information of the networked system. A subset of the known vulnerabilities may be determined to be relevant to the networked system based on correlations between the vulnerabilities and the network information. The networked system may also be analyzed from an internal perspective to determine impacts of the relevant known vulnerabilities to the networked system. The impact of a vulnerability may be determined based on the type of data and/or the type of services that may be accessible in an attack that exploits the vulnerability. The vulnerabilities may then be ranked and addressed based on the impacts.

Abstract: A method for using a malware and phishing detection and mediation platform is discussed. The method includes accessing data from one or more of a monitored portion of website data and a monitored portion of emails, the data indicating a respective potential malware or a suspect phishing element (e.g., Uniform Resource Locator (URL)). The method includes selecting one of a plurality of detection engines for processing the data, where the selecting is based on previous results of previous processing by one or more detection engines. Each of the plurality of detection engines can be for performing one or more respective investigation actions on the plurality of data to determine a particular issue with one of the monitored data. The method also includes determining a mediation action based on a result of processing of the detection engine and the previous processing.

Abstract: Systems and methods of authenticating voice data using a ledger (blockchain). Examples include a scalable and seamless system that uses blockchain technologies to distribute trust of a conversation, authenticate persons in a conversation, track their characteristics and also to keep records of conversations. In some examples, smart phones, wearables, and Internet-of-Things (IoT) devices can be used to record and track conversations between individuals. These devices can each be used to create entries for the blockchain or a single device could be used to keep track of the entirety of the conversation. Fuzzy hashing may be used to compare newly created entries with previous entries on the ledger.

Abstract: A method for phishing detection using certificates associated with uniform resource locators (URLs) is discussed. The method includes accessing certificate portions of a certificate associated with a suspect URL, the certificate accessed at a database that includes certificates obtained by monitoring certificate logs. The method includes accessing a URL score for the suspect URL. The method includes assigning a certificate rule score based on partial certificate scores of certificate portions, the certificate rule score indicating a phishing potential for the certificate, each of the partial certificate scores indicating a likelihood of phishing of each portion based on certificate rules. The method includes using a machine learning model based on the URL score and the certificate to determine a uniqueness certificate score. The method also includes determining a phishing certificate score based on the certificate rule score and the uniqueness certificate score for the certificate.

Abstract: There are provided systems and methods for actionable insight into user interaction data. A service provider server can access user interaction data associated with an interaction between a first communication device and the service provider server, and generates feature representations of the user interaction data, in which the feature representations respectively correspond to extracted features that include textual data features or audio data features. The service provider server can determine an intent of the interaction from the feature representations using a machine learning-trained classifier, in which the intent corresponds to a first actionable insight category. The interaction is mapped to a first cluster based on the intent, and the service provider server issues a remedial action for the interaction based on the mapping of the interaction to the first cluster, in which the remedial action is associated with a particular type of activity in the first actionable insight category.

Abstract: There are provided systems and methods for a voice vector framework that authenticates user interactions. A service provider server receives user interaction data having audio data that is associated with an interaction between a user device and the service provider server. The server extracts user attributes from the audio data and obtains user account information associated with the user device. The server selects a classifier that corresponds to a select combination of features based on the user account information and applies the classifier to the user attributes. The server generates a voice vector that includes multiple scores indicating likelihoods that a respective user attribute corresponds to an attribute of the select combination of features. The server compares the voice vector to a baseline vector corresponding to a predetermined combination of features and sends a notification to an agent device with an indication of whether the user device is verified.

Abstract: Methods and systems are presented for dynamically adjusting a risk classification of a risk source based on classifications of one or more other risk sources. The risk engine may first classify a first risk source as a first risk type based on an initial analysis of the first risk source. Subsequent to classifying the first risk source as the first risk type, the risk engine may determine that a second risk source is associated with a second risk type. Based on the determination that the second risk source is associated with the second risk type, the risk engine may re-classify the first risk source as the second risk type. The risk engine may then use the reclassification of the first risk source to improve network security of an online service provider.

Abstract: Techniques are disclosed relating to generating trained machine learning modules to identify whether user interfaces accessed by a computing device match user interfaces associated with a set of Internet domain names. A server computer system receives a set of Internet domain names and generates screenshots for user interfaces associated with the set of Internet domain names. The server computer system then trains machine learning modules that are customized for the set of Internet domain names using the screenshots. The server then transmits the machine learning modules to the computing device, where the machine learning modules are usable by an application executing on the computing device to identify whether a user interface accessed by the device matches a user interface associated with the set of Internet domain names. Such techniques may advantageously allow servers to identify whether user interfaces are suspicious without introducing latency and increased page load times.

Abstract: A computer system detects an action corresponding to a resource page being rendered within a web view of an application. In response to the detecting the action corresponding to a resource page being rendered within the web view of the application, the computer system identifies information associated with the resource page and determines if one or more risk indications correspond to the identified information. In response to determining that one or more risk indications correspond to the identified information, the computer system implements one or more security measures.

Abstract: Methods and systems are presented for dynamically adjusting a risk classification of a risk source based on classifications of one or more other risk sources. The risk engine may first classify a first risk source as a first risk type based on an initial analysis of the first risk source. Subsequent to classifying the first risk source as the first risk type, the risk engine may determine that a second risk source is associated with a second risk type. Based on the determination that the second risk source is associated with the second risk type, the risk engine may re-classify the first risk source as the second risk type. The risk engine may then use the reclassification of the first risk source to improve network security of an online service provider.