Abstract: IPRID reputation assessment enhances cybersecurity. IPRIDs include IP addresses, domain names, and other network resource identities. A convolutional neural network or other machine learning model is trained with data including aggregate features or rollup features or both. Aggregate features may include aggregated submission counts, classification counts, HTTP code counts, detonation statistics, and redirect counts, for instance. Rollup features reflect hierarchical rollups of data using <unknown> value placeholders specified in IPRID templates. The trained model can predictively infer a label, or produce a rapid lookup table of IPRIDs and maliciousness probabilities. Training data may be organized in grids with rows, columns, planes, branches, and slots. Training data may include whois data, geolocation data, and tenant data. Training data tuple sets may be expanded by date or by original IPRID.

Abstract: Cybersecurity enhancements help avoid malicious Uniform Resource Locators (URLs). Embodiments may reduce or eliminate reliance on subjective analysis or detonation virtual machines. URL substrings are automatically analyzed for maliciousness using malice patterns. Patterns may test counts, lengths, rarity, encodings, and other inherent aspects of URLs. URLs may be analyzed individually, or in groups to detect shared portions, or both. URL analysis may use or avoid machine learning, and may use or avoid lookups. Malice patterns may be used individually or in combinations to detect malicious URLs. Analysis results may enhance security through blocking use of suspect URLs, flagging them for further analysis, or allowing their validated use, for instance. Analysis results may also be fed back to further train a machine learning model or a statistical model.

Abstract: IPRID reputation assessment enhances cybersecurity. IPRIDs include IP addresses, domain names, and other network resource identities. A convolutional neural network or other machine learning model is trained with data including aggregate features or rollup features or both. Aggregate features may include aggregated submission counts, classification counts, HTTP code counts, detonation statistics, and redirect counts, for instance. Rollup features reflect hierarchical rollups of data using <unknown> value placeholders specified in IPRID templates. The trained model can predictively infer a label, or produce a rapid lookup table of IPRIDs and maliciousness probabilities. Training data may be organized in grids with rows, columns, planes, branches, and slots. Training data may include whois data, geolocation data, and tenant data. Training data tuple sets may be expanded by date or by original IPRID.

Abstract: Cybersecurity enhancements help avoid malicious Uniform Resource Locators (URLs). Embodiments may reduce or eliminate reliance on subjective analysis or detonation virtual machines. URL substrings are automatically analyzed for maliciousness using malice patterns. Patterns may test counts, lengths, rarity, encodings, and other inherent aspects of URLs. URLs may be analyzed individually, or in groups to detect shared portions, or both. URL analysis may use or avoid machine learning, and may use or avoid lookups. Malice patterns may be used individually or in combinations to detect malicious URLs. Analysis results may enhance security through blocking use of suspect URLs, flagging them for further analysis, or allowing their validated use, for instance. Analysis results may also be fed back to further train a machine learning model or a statistical model.

Abstract: Potential linguistic errors within a sequence of words of a sentence are identified based on analysis of a configurable sliding window. The analysis is performed based on an assumption that if a sequence of words occurs frequently enough within a large, well-formed corpus, its joint probability for occurring in a sentence is very likely to be greater than the same words randomly ordered.

Abstract: The present disclosure is directed to a method of verifying a compound word. The method includes receiving an input signal indicative of a textual input and accessing a rule and a lexical data structure from data stores. The rule is applied to the textual input to determine whether the textual input is a valid compound word. An output signal is provided that is indicative of whether the textual input is a compound word.

Abstract: Potential linguistic errors within a sequence of words of a sentence are identified based on analysis of a configurable sliding window. The analysis is performed based on an assumption that if a sequence of words occurs frequently enough within a large, well-formed corpus, its joint probability for occurring in a sentence is very likely to be greater than the same words randomly ordered.

Abstract: Potential linguistic errors within a sequence of words of a sentence are identified based on analysis of a configurable sliding window. The analysis is performed based on an assumption that if a sequence of words occurs frequently enough within a large, well-formed corpus, its joint probability for occurring in a sentence is very likely to be greater than the same words randomly ordered.

Abstract: Systems, methods, and computer media for efficiently processing user log data are provided. The log data is progressively processed in variable sized windows based on a specified time period. The log data may be anonymized to protect user privacy. A log server processes the windowed log data in phases. The first phase includes fast data like page view log data. Subsequent phases include slow data like session data which may build on the page view data processed in the first phase. The log server identifies metrics based on the log data processed at each phase. Based on the identified metrics, the log server may identify interests across a community of users or for specific users.

Abstract: The present disclosure is directed to a method of verifying a compound word. The method includes receiving an input signal indicative of a textual input and accessing a rule and a lexical data structure from data stores. The rule is applied to the textual input to determine whether the textual input is a valid compound word. An output signal is provided that is indicative of whether the textual input is a compound word.

Abstract: The present disclosure is directed to a method of verifying a compound word. The method includes receiving an input signal indicative of a textual input and accessing a rule and a lexical data structure from data stores. The rule is applied to the textual input to determine whether the textual input is a valid compound word. An output signal is provided that is indicative of whether the textual input is a compound word.

Abstract: Potential linguistic errors within a sequence of words of a sentence are identified based on analysis of a configurable sliding window. The analysis is performed based on an assumption that if a sequence of words occurs frequently enough within a large, well-formed corpus, its joint probability for occurring in a sentence is very likely to be greater than the same words randomly ordered.

Abstract: The present disclosure is directed to a method of verifying a compound word. The method includes receiving an input signal indicative of a textual input and accessing a rule and a lexical data structure from data stores. The rule is applied to the textual input to determine whether the textual input is a valid compound word. An output signal is provided that is indicative of whether the textual input is a compound word.