Abstract: In one embodiment, a device generates one or more time series of characteristics of client-server communications observed in a network for a particular client in the network. The device partitions the one or more time series into sets of time windows based on patterns present in the characteristics of the client-server communications. The device compares the characteristics of the client-server communications from the partitioned time windows to determine measures of behavioral similarity between the compared time windows. The device provides the measures of behavioral similarity between the compared time windows as input to a machine learning-based malware detector. The device causes performance of a mitigation action in the network when the machine learning-based malware detector determines that the particular client in the network is infected with malware.

Abstract: In one embodiment, a device generates one or more time series of characteristics of client-server communications observed in a network for a particular client in the network. The device partitions the one or more time series into sets of time windows based on patterns present in the characteristics of the client-server communications. The device compares the characteristics of the client-server communications from the partitioned time windows to determine measures of behavioral similarity between the compared time windows. The device provides the measures of behavioral similarity between the compared time windows as input to a machine learning-based malware detector. The device causes performance of a mitigation action in the network when the machine learning-based malware detector determines that the particular client in the network is infected with malware.

Abstract: An interactive communication system configured to conduct a call with a caller. The interactive communication system comprises at least one computer hardware processor configured to perform: obtaining a plurality of dialog chunks comprising information provided by the caller to the interactive communication system and information provided by the interactive communication system to the caller; generating, based on the plurality of dialog chunks, a respective plurality of feature sets, each of the plurality of feature sets comprising at least one feature generated using a respective dialog chunk of the plurality of dialog chunks; determining, based on the plurality of feature sets, a respective plurality of dialog chunk scores; determining, based at least in part on the plurality of dialog chunk scores, a likelihood that the caller is dissatisfied with the interactive communication system; and when the likelihood exceeds a threshold, performing a remedial action that alters how the call is handled.

Abstract: In one embodiment, a method includes receiving at a security analysis device a plurality of indicators of compromise (IOCs) associated with an entity, sorting at the security analysis device, the IOCs based on a time of occurrence of each of the IOCs, creating a representation of transitions between the IOCs at the security analysis device, and generating at the security analysis device, a feature vector based on the representation of transitions. The feature vector is configured for use by a classifier in identifying malicious entities. An apparatus and logic are also disclosed herein.

Abstract: In one embodiment, a security device identifies, from monitored network traffic of one or more users, one or more suspicious domain names as candidate domains, the one or more suspicious domain names identified based on an occurrence of linguistic units used in discovered domain names within the monitored network traffic. The security device may then determine one or more features of the candidate domains, and confirms certain domains of the candidate domains as malicious domains using a parameterized classifier against the one or more features.

Abstract: An interactive communication system configured to conduct a call with a caller. The interactive communication system comprises at least one computer hardware processor configured to perform: obtaining a plurality of dialog chunks comprising information provided by the caller to the interactive communication system and information provided by the interactive communication system to the caller; generating, based on the plurality of dialog chunks, a respective plurality of feature sets, each of the plurality of feature sets comprising at least one feature generated using a respective dialog chunk of the plurality of dialog chunks; determining, based on the plurality of feature sets, a respective plurality of dialog chunk scores; determining, based at least in part on the plurality of dialog chunk scores, a likelihood that the caller is dissatisfied with the interactive communication system; and when the likelihood exceeds a threshold, performing a remedial action that alters how the call is handled.

Abstract: Designing a dialog application is a difficult task that typically requires a complete understanding of the dialog framework and a high level of expertise to map system requirements to the actual implementations. In contrast, determining the logic of the dialog application via sample interaction is typically very simple and efficient. A developer can describe via speech or text what the operations of the application are, effectively writing dialog samples. Methods described herein reverse the way dialog applications are designed by obtaining annotated dialog samples and defined concepts related to a requested dialog application; analyzing the annotated dialog samples, defined concepts, and one or more relationships between or among the defined concepts; and generating an executable dialog application based on the analysis of the annotated dialog samples and the defined concepts.

Abstract: An automatic conversational system has multiple computer-implemented dialogue components for conducting an automated dialogue process with a human user. A user client delivers dialogue output prompts to the human user and receives dialogue input responses from the human user including speech inputs. An automatic speech recognition engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) processing arrangement processes the dialogue input responses and the text words to determine corresponding semantic interpretations. The NLU processing arrangement includes an anaphora processor that accesses different information sources characterizing dialogue context, linguistic features, and NLU features to identify unresolved anaphora in the text words needing resolution in order to determine a semantic interpretation. A dialogue manager manages the dialogue process with the human user based on the semantic interpretations.

Abstract: An automatic conversational system has multiple computer-implemented dialogue components for conducting an automated dialogue process with a human user. A user client delivers dialogue output prompts to the human user and receives dialogue input responses from the human user including speech inputs. An automatic speech recognition engine processes the speech inputs to determine corresponding sequences of representative text words. A natural language understanding (NLU) processing arrangement processes the dialogue input responses and the text words to determine corresponding semantic interpretations. The NLU processing arrangement includes an anaphora processor that accesses different information sources characterizing dialogue context, linguistic features, and NLU features to identify unresolved anaphora in the text words needing resolution in order to determine a semantic interpretation. A dialogue manager manages the dialogue process with the human user based on the semantic interpretations.