Abstract: A computer-implemented method of generating security awareness training samples may include receiving, structuring and storing information about a user of an organization into an organization knowledge base and selecting and fetching at least one training template, each including an electronic message template and a prompt template for a large language model. The prompt template(s) and the electronic message template(s) may then be specialized using the information about users of the organization stored in the organization knowledge base. The specialized prompt template(s) may then be submitted to a large language model artificial intelligence system. Textual content may then be received from the large language model responsive to each submitted specialized prompt template.

Abstract: A computer-implemented method of detecting synthetic text in an email may determine whether the sender of a received email is a named individual and if so, may extract textual content from the email. When the size of the extracted textual content is at least equal to a configurable size threshold, the extracted textual content may be analyzed using a plurality of synthetic text detection engines, each being configured to generate a synthetic text detection score. The synthetic text detection scores may then be compared to a respective score threshold associated with each of the synthetic text detection engines. An action to warn and/or protect the recipient of the email may be carried out when one or more synthetic text detection scores is at least equal to a respective associated score threshold, which is indicative of a detection of synthetic textual content in the received email.

Abstract: A computer-implemented method of generating metadata from an image may comprise sending the image to an object detection service, which generates detections metadata from the image. The image may also be sent to a visual features extractor, which extracts visual features metadata from the image. The generated detections metadata may then be sent to an uncertainty score calculator, which computes an uncertainty score from the detections metadata. The uncertainty score may be related to a level of uncertainty within the detections metadata. The image, the visual features metadata, the detections metadata and the uncertainty score may then be stored in a database accessible over a computer network.

Abstract: A computer-implemented method of generating metadata from an image may comprise sending the image to an object detection service, which generates detections metadata from the image. The image may also be sent to a visual features extractor, which extracts visual features metadata from the image. The generated detections metadata may then be sent to an uncertainty score calculator, which computes an uncertainty score from the detections metadata. The uncertainty score may be related to a level of uncertainty within the detections metadata. The image, the visual features metadata, the detections metadata and the uncertainty score may then be stored in a database accessible over a computer network.

Abstract: A computer-implemented method of detecting logos in a graphical rendering may comprise detecting, using a first and a second trained object detector, logos in the graphical rendering and outputting a first and a second list of detections and filtering, using at least a first and a second prior performance-based filter, the received first and second lists of detections into a first group of kept detections, a second group of discarded detections and a third group of detections. Detections in the third group of detections may be clustered in at least one cluster comprising detections that are of a same class and that are generally co-located within the electronic image. A cluster score may then be assigned to each cluster. A set of detections of logos in the graphical rendering may then be output, the set comprising the detections in the first group and a detection from each of the clusters whose assigned cluster score is greater than a respective threshold.

Abstract: A computer-implemented method of detecting logos in a graphical rendering may comprise detecting, using a first and a second trained object detector, logos in the graphical rendering and outputting a first and a second list of detections and filtering, using at least a first and a second prior performance-based filter, the received first and second lists of detections into a first group of kept detections, a second group of discarded detections and a third group of detections. Detections in the third group of detections may be clustered in at least one cluster comprising detections that are of a same class and that are generally co-located within the electronic image. A cluster score may then be assigned to each cluster. A set of detections of logos in the graphical rendering may then be output, the set comprising the detections in the first group and a detection from each of the clusters whose assigned cluster score is greater than a respective threshold.

Abstract: A computer-implemented method of generating metadata from an image may comprise sending the image to an object detection service, which generates detections metadata from the image. The image may also be sent to a visual features extractor, which extracts visual features metadata from the image. The generated detections metadata may then be sent to an uncertainty score calculator, which computes an uncertainty score from the detections metadata. The uncertainty score may be related to a level of uncertainty within the detections metadata. The image, the visual features metadata, the detections metadata and the uncertainty score may then be stored in a database accessible over a computer network.

Abstract: A computer-implemented method of detecting logos in a graphical rendering may comprise detecting, using a first and a second trained object detector, logos in the graphical rendering and outputting a first and a second list of detections and filtering, using at least a first and a second prior performance-based filter, the received first and second lists of detections into a first group of kept detections, a second group of discarded detections and a third group of detections. Detections in the third group of detections may be clustered in at least one cluster comprising detections that are of a same class and that are generally co-located within the electronic image. A cluster score may then be assigned to each cluster. A set of detections of logos in the graphical rendering may then be output, the set comprising the detections in the first group and a detection from each of the clusters whose assigned cluster score is greater than a respective threshold.

Abstract: A computer-implemented method may comprise collecting and storing a plurality of electronic messages and a corresponding plurality of phishing kits, each of which being associated with one or several malicious Uniform Resource Locator (URL) and extracting a set of features from each of the plurality of electronic messages. For each of the extracted set of features, the method may comprise determining a set of optimal scanning parameters using one or more decision trees, trained with a supervised learning algorithm based on programmatically or manually examining or reverse-engineering the source code of the phishing kits, or trained with a supervised learning algorithm based on a function that iteratively requests data from the websites pointed to by the malicious URLs and examines data and codes returned by such requests.

Abstract: A computer-implemented method of detecting logos in a graphical rendering may comprise detecting, using a first and a second trained object detector, logos in the graphical rendering and outputting a first and a second list of detections and filtering, using at least a first and a second prior performance-based filter, the received first and second lists of detections into a first group of kept detections, a second group of discarded detections and a third group of detections. Detections in the third group of detections may be clustered in at least one cluster comprising detections that are of a same class and that are generally co-located within the electronic image. A cluster score may then be assigned to each cluster. A set of detections of logos in the graphical rendering may then be output, the set comprising the detections in the first group and a detection from each of the clusters whose assigned cluster score is greater than a respective threshold.

Abstract: A computer-implemented method of generating an augmented electronic text document comprises establishing a directed multigraph where each vertex is associated with a separate language and is connected to at least one other one of the vertices by an oriented edge indicative of a machine translation engine's ability to translate between languages associated with the vertices connected by the oriented edge with acceptable performance. The directed multigraph is then traversed starting at a predetermined origin vertex associated with an original language of the original electronic text document by randomly selecting an adjacent vertex pointed to by an oriented edge connected to the predetermined origin vertex and causing a machine translation engine to translate the original electronic text document from the original language to a language associated with the selected vertex.

Abstract: A computer-implemented method may comprise collecting and storing a plurality of electronic messages and a corresponding plurality of phishing kits, each of which being associated with one or several malicious Uniform Resource Locator (URL) and extracting a set of features from each of the plurality of electronic messages. For each of the extracted set of features, the method may comprise determining a set of optimal scanning parameters using one or more decision trees, trained with a supervised learning algorithm based on programmatically or manually examining or reverse-engineering the source code of the phishing kits, or trained with a supervised learning algorithm based on a function that iteratively requests data from the websites pointed to by the malicious URLs and examines data and codes returned by such requests.

Abstract: A computer-implemented method of detecting obfuscated code in an electronic message's attachment may comprise receiving, over a computer network, an electronic message comprising an attachment; determining the file type of the attachment; extracting one or more scripts from the attachment, computing a distance measure between selected one or more features of the extracted one or more scripts and corresponding one or more selected features of scripts of a model corpus of non-obfuscated script files and comparing the computed distance measure with a threshold. When the computed distance measure is at least as great as the threshold, it may be determined that the extracted one or more scripts comprise obfuscated code and a defensive action with respect to at least the attachment may be taken. When the computed distance measure is less than the threshold, it may be determined that the extracted one or more scripts does not comprise obfuscated code.

Abstract: A computer-implemented method of generating an augmented electronic text document comprises establishing a directed multigraph where each vertex is associated with a separate language and is connected to at least one other one of the vertices by an oriented edge indicative of a machine translation engine's ability to translate between languages associated with the vertices connected by the oriented edge with acceptable performance. The directed multigraph is then traversed starting at a predetermined origin vertex associated with an original language of the original electronic text document by randomly selecting an adjacent vertex pointed to by an oriented edge connected to the predetermined origin vertex and causing a machine translation engine to translate the original electronic text document from the original language to a language associated with the selected vertex.

Abstract: A computer-implemented method may comprise collecting and storing a plurality of electronic messages and a corresponding plurality of phishing kits, each of which being associated with one or several malicious Uniform Resource Locator (URL) and extracting a set of features from each of the plurality of electronic messages. For each of the extracted set of features, the method may comprise determining a set of optimal scanning parameters using one or more decision trees, trained with a supervised learning algorithm based on programmatically or manually examining or reverse-engineering the source code of the phishing kits, or trained with a supervised learning algorithm based on a function that iteratively requests data from the websites pointed to by the malicious URLs and examines data and codes returned by such requests.

Abstract: A computer-implemented method of generating an augmented electronic text document comprises establishing a directed multigraph where each vertex is associated with a separate language and is connected to at least one other one of the vertices by an oriented edge indicative of a machine translation engine's ability to translate between languages associated with the vertices connected by the oriented edge with acceptable performance. The directed multigraph is then traversed starting at a predetermined origin vertex associated with an original language of the original electronic text document by randomly selecting an adjacent vertex pointed to by an oriented edge connected to the predetermined origin vertex and causing a machine translation engine to translate the original electronic text document from the original language to a language associated with the selected vertex.

Abstract: A computer-implemented method of generating an augmented electronic text document comprises establishing a directed multigraph where each vertex is associated with a separate language and is connected to at least one other one of the vertices by an oriented edge indicative of a machine translation engine's ability to translate between languages associated with the vertices connected by the oriented edge with acceptable performance The directed multigraph is then traversed starting at a predetermined origin vertex associated with an original language of the original electronic text document by randomly selecting an adjacent vertex pointed to by an oriented edge connected to the predetermined origin vertex and causing a machine translation engine to translate the original electronic text document from the original language to a language associated with the selected vertex.

Abstract: A computer-implemented method of generating an augmented electronic text document comprises establishing a directed multigraph where each vertex is associated with a separate language and is connected to at least one other one of the vertices by an oriented edge indicative of a machine translation engine's ability to translate between languages associated with the vertices connected by the oriented edge with acceptable performance. The directed multigraph is then traversed starting at a predetermined origin vertex associated with an original language of the original electronic text document by randomly selecting an adjacent vertex pointed to by an oriented edge connected to the predetermined origin vertex and causing a machine translation engine to translate the original electronic text document from the original language to a language associated with the selected vertex.

Abstract: A computer-implemented method of generating an augmented electronic text document comprises establishing a directed multigraph where each vertex is associated with a separate language and is connected to at least one other one of the vertices by an oriented edge indicative of a machine translation engine's ability to translate between languages associated with the vertices connected by the oriented edge with acceptable performance. The directed multigraph is then traversed starting at a predetermined origin vertex associated with an original language of the original electronic text document by randomly selecting an adjacent vertex pointed to by an oriented edge connected to the predetermined origin vertex and causing a machine translation engine to translate the original electronic text document from the original language to a language associated with the selected vertex.