Abstract: Systems and methods for anonymizing personal information are provided. A set of data may be received that includes a first subset of data that corresponds to a unique identifier, a second subset of data that corresponds to a timestamp, and a third subset of data that corresponds to a personal identifiable information (PII) record. The PII record may include a plurality of fields, one or more of which may include values. The set of data may be pre-processed. A respective embedding may be generated, for each value of a respective field, of the plurality of fields, that includes a value. The pre-processed set of data may be anonymized to generate an anonymized set of data, in the form of a graph structure. The graph structure may be stored in a database, and a fraud risk model may be trained, based on the database.

Abstract: Aspects of the present disclosure are operable to protect against malicious objects, such as JavaScript code, which may be encountered, downloaded, or otherwise accessed from a content source by a computing system. In an example, antivirus software implementing aspects disclosed herein may be capable of detecting malicious objects in real-time. Aspects of the present disclosure aim to reduce the amount of time used to detect malicious code while maintaining detection accuracy, as detection delays and/or a high false positive rate may result in a negative user experience. Among other benefits, the systems and methods disclosed herein are operable to identify malicious objects encountered by a computing system while maintaining a high detection rate, a low false positive rate, and a high scanning speed.

Abstract: Aspects of the present disclosure are operable to protect against malicious objects, such as JavaScript code, which may be encountered, downloaded, or otherwise accessed from a content source by a computing system. In an example, antivirus software implementing aspects disclosed herein may be capable of detecting malicious objects in real-time. Aspects of the present disclosure aim to reduce the amount of time used to detect malicious code while maintaining detection accuracy, as detection delays and/or a high false positive rate may result in a negative user experience. Among other benefits, the systems and methods disclosed herein are operable to identify malicious objects encountered by a computing system while maintaining a high detection rate, a low false positive rate, and a high scanning speed.

Abstract: Aspects of the present disclosure relate to threat detection of executable files. A plurality of static data points may be extracted from an executable file without decrypting or unpacking the executable file. The executable file may then be analyzed without decrypting or unpacking the executable file. Analysis of the executable file may comprise applying a classifier to the plurality of extracted static data points. The classifier may be trained from data comprising known malicious executable files, known benign executable files and known unwanted executable files. Based upon analysis of the executable file, a determination can be made as to whether the executable file is harmful.

Abstract: Aspects of the present disclosure relate to threat detection of executable files. A plurality of static data points may be extracted from an executable file without decrypting or unpacking the executable file. The executable file may then be analyzed without decrypting or unpacking the executable file. Analysis of the executable file may comprise applying a classifier to the plurality of extracted static data points. The classifier may be trained from data comprising known malicious executable files, known benign executable files and known unwanted executable files. Based upon analysis of the executable file, a determination can be made as to whether the executable file is harmful.

Abstract: Examples of the present disclosure describe systems and methods of automatic inline detection based on static data. In aspects, a file being received by a recipient device may be analyzed using an inline parser. The inline parser may identify sections of the file and feature vectors may be created for the identified sections. The feature vectors may be used to calculate a score corresponding to the malicious status of the file as the information is being analyzed. If a score is determined to exceed a predetermined threshold, the file download process may be terminated. In aspects, the received files, file fragments, feature vectors and/or additional data may be collected and analyzed to build a probabilistic model used to identify potentially malicious files.

Abstract: Aspects of the present disclosure are operable to protect against malicious objects, such as JavaScript code, which may be encountered, downloaded, or otherwise accessed from a content source by a computing system. In an example, antivirus software implementing aspects disclosed herein may be capable of detecting malicious objects in real-time. Aspects of the present disclosure aim to reduce the amount of time used to detect malicious code while maintaining detection accuracy, as detection delays and/or a high false positive rate may result in a negative user experience. Among other benefits, the systems and methods disclosed herein are operable to identify malicious objects encountered by a computing system while maintaining a high detection rate, a low false positive rate, and a high scanning speed.

Abstract: Examples of the present disclosure describe systems and methods of automatic inline detection based on static data. In aspects, a file being received by a recipient device may be analyzed using an inline parser. The inline parser may identify sections of the file and feature vectors may be created for the identified sections. The feature vectors may be used to calculate a score corresponding to the malicious status of the file as the information is being analyzed. If a score is determined to exceed a predetermined threshold, the file download process may be terminated. In aspects, the received files, file fragments, feature vectors and/or additional data may be collected and analyzed to build a probabilistic model used to identify potentially malicious files.

Abstract: Aspects of the present disclosure are operable to protect against malicious objects, such as JavaScript code, which may be encountered, downloaded, or otherwise accessed from a content source by a computing system. In an example, antivirus software implementing aspects disclosed herein may be capable of detecting malicious objects in real-time. Aspects of the present disclosure aim to reduce the amount of time used to detect malicious code while maintaining detection accuracy, as detection delays and/or a high false positive rate may result in a negative user experience. Among other benefits, the systems and methods disclosed herein are operable to identify malicious objects encountered by a computing system while maintaining a high detection rate, a low false positive rate, and a high scanning speed.

Abstract: Aspects of the present disclosure relate to threat detection of executable files. A plurality of static data points may be extracted from an executable file without decrypting or unpacking the executable file. The executable file may then be analyzed without decrypting or unpacking the executable file. Analysis of the executable file may comprise applying a classifier to the plurality of extracted static data points. The classifier may be trained from data comprising known malicious executable files, known benign executable files and known unwanted executable files. Based upon analysis of the executable file, a determination can be made as to whether the executable file is harmful.

Abstract: Aspects of the present disclosure relate to threat detection of executable files. A plurality of static data points may be extracted from an executable file without decrypting or unpacking the executable file. The executable file may then be analyzed without decrypting or unpacking the executable file. Analysis of the executable file may comprise applying a classifier to the plurality of extracted static data points. The classifier may be trained from data comprising known malicious executable files, known benign executable files and known unwanted executable files. Based upon analysis of the executable file, a determination can be made as to whether the executable file is harmful.

Abstract: Aspects of the present disclosure are operable to protect against malicious objects, such as JavaScript code, which may be encountered, downloaded, or otherwise accessed from a content source by a computing system. In an example, antivirus software implementing aspects disclosed herein may be capable of detecting malicious objects in real-time. Aspects of the present disclosure aim to reduce the amount of time used to detect malicious code while maintaining detection accuracy, as detection delays and/or a high false positive rate may result in a negative user experience. Among other benefits, the systems and methods disclosed herein are operable to identify malicious objects encountered by a computing system while maintaining a high detection rate, a low false positive rate, and a high scanning speed.

Abstract: Examples of the present disclosure describe systems and methods of automatic inline detection based on static data. In aspects, a file being received by a recipient device may be analyzed using an inline parser. The inline parser may identify sections of the file and feature vectors may be created for the identified sections. The feature vectors may be used to calculate a score corresponding to the malicious status of the file as the information is being analyzed. If a score is determined to exceed a predetermined threshold, the file download process may be terminated. In aspects, the received files, file fragments, feature vectors and/or additional data may be collected and analyzed to build a probabilistic model used to identify potentially malicious files.

Abstract: Aspects of the present disclosure relate to threat detection of executable files. A plurality of static data points may be extracted from an executable file without decrypting or unpacking the executable file. The executable file may then be analyzed without decrypting or unpacking the executable file. Analysis of the executable file may comprise applying a classifier to the plurality of extracted static data points. The classifier may be trained from data comprising known malicious executable files, known benign executable files and known unwanted executable files. Based upon analysis of the executable file, a determination can be made as to whether the executable file is harmful.

Abstract: An efficient method and system to enhance digital acquisition devices for analog data is presented. The enhancements offered by the method and system are available to the user in local as well as in remote deployments yielding efficiency gains for a large variety of business processes. The quality enhancements of the acquired digital data are achieved efficiently by employing virtual reacquisition. The method of virtual reacquisition renders unnecessary the physical reacquisition of the analog data in case the digital data obtained by the acquisition device are of insufficient quality. The method and system allows multiple users to access the same acquisition device for analog data. In some embodiments, one or more users can virtually reacquire data provided by multiple analog or digital sources. The acquired raw data can be processed by each user according to his personal preferences and/or requirements.

Abstract: An efficient method and system to enhance digital acquisition devices for analog data is presented. The enhancements offered by the method and system are available to the user in local as well as in remote deployments yielding efficiency gains for a large variety of business processes. The quality enhancements of the acquired digital data are achieved efficiently by employing virtual reacquisition. The method of virtual reacquisition renders unnecessary the physical reacquisition of the analog data in case the digital data obtained by the acquisition device are of insufficient quality. The method and system allows multiple users to access the same acquisition device for analog data. In some embodiments, one or more users can virtually reacquire data provided by multiple analog or digital sources. The acquired raw data can be processed by each user according to his personal preferences and/or requirements.

Abstract: A method and system for delineating document boundaries and identifying document types by analyzing digital images of one or more documents, automatically categorizing one or more pages or subdocuments within the one or more documents and automatically generating delineation identifiers, such as computer-generated images of separation pages inserted between digital images belonging to different categories, a description of the categorization sequence of the digital images, or a computer-generated electronic label affixed or associated with said digital images.