Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing of a Uniform Resource Locator (URL) to identify a plurality of key-value pairs in a query string of the URL. The plurality of key-value pairs may include one or more potential anonymous identifiers. In an example embodiment, a machine learning algorithm is trained on the URL to determine whether the one or more potential anonymous identifiers are actual anonymous identifiers (i.e., advertising identifiers) that provide advertisers a method to identify a user device without using, for example, a permanent device identifier. In this embodiment, a ranking threshold is used to verify the URL. A verified URL associate the one or more potential anonymous identifiers with the user device as actual anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing network traffic to automatically recognize anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic, and to identify topics relevant to a user of a particular network device so that communications to such a user are more likely to relate to a topic of interest to the user.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing network traffic to automatically recognize anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic, and to identify topics relevant to a user of a particular network device so that communications to such a user are more likely to relate to a topic of interest to the user.

Abstract: A computing system may automatically determine if data traffic of an individual network cell, or other relatively small portion of a network, is sufficiently complex so as to desirably maintain privacy of individual users of the network. Data traffic, which may include phone calls, text messaging, Internet browsing, and so on, of a network cell in a rural area may experience a relatively low volume of data traffic. On the other hand, a cell tower in a city may experience data traffic of hundreds or so individual users during a one-day period. Data traffic of such relatively high volume may be sufficiently complex and may be aggregated and used for data analytics.

Abstract: A computing system may automatically determine if data traffic of an individual network cell, or other relatively small portion of a network, is sufficiently complex so as to desirably maintain privacy of individual users of the network. Data traffic, which may include phone calls, text messaging, Internet browsing, and so on, of a network cell in a rural area may experience a relatively low volume of data traffic. On the other hand, a cell tower in a city may experience data traffic of hundreds or so individual users during a one-day period. Data traffic of such relatively high volume may be sufficiently complex and may be aggregated and used for data analytics.

Abstract: A computing system may automatically anonymize network transaction data of a network transaction by removing a portion of the uniform resource locator (URL) associated with the network transaction. Such anonymization may be beneficial by allowing for the network transaction data to be used (e.g., by third parties) for data analytics, for example, while securing user identities by removing personal information or the identities of individual users engaged in such network transactions. In some examples, network transactions may include phone calls and conversations, video conferencing, text messaging, Internet ac (e.g., file sharing and streaming), and so on.

Abstract: A computing system may automatically infer one or more events that occur during an application session involving activity on a network, such as the Internet. Such an application session may be interactions with, for example, social networking websites, banking websites, news websites, and so on. Events are any of a number of activities or transactions that may occur during the application session. The computing system may automatically infer an event by gathering network transaction data for network transactions performed by one or more client devices of a wireless communication network. The computing system may generate a network activity signature based, at least in part, on the network transaction data and apply pattern recognition and/or machine learning to the network activity signature to infer events associated with the network activity signature.

Abstract: Techniques for understanding a user's intentions when the user is searching web sites on the Internet are disclosed. Although search queries are typically encrypted so they cannot be understood by entities other than the user and a host of a search engine being used, the present techniques describe ways that a third party can infer user intentions from encrypted activity. Determination of user intentions in ways described herein can be used to provide content to a user that may be of particular interest to the user. Furthermore, provision of such content is thereby not limited to a host of a search engine, as is typically the case when only the host can comprehend content of search queries.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing network traffic to automatically recognize anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic, and to identify topics relevant to a user of a particular network device so that communications to such a user are more likely to relate to a topic of interest to the user.

Abstract: The implementation of anonymization validation protects the privacy of subscribers that uses the telecommunication services of a wireless telecommunication network. The anonymization validation checks the data in an anonymized communication record table to ensure the data is properly filtered or encrypted. The anonymized communication record table contains data pertaining to at least one of telephone calls, messages, and data connectivity sessions that are initiated or received by multiple subscribers of a wireless telecommunication network. The anonymized communication record is generated from an original communication record table via filtering out or encryption of the data pertaining to one or more subscribers. The performance of the anonymization validation includes performing opt-out filtering validation, network cell anonymity filtering validation, and telephone number encryption validation on the anonymized communication record table.

Abstract: The implementation of anonymization validation protects the privacy of subscribers that uses the telecommunication services of a wireless telecommunication network. The anonymization validation checks the data in an anonymized communication record table to ensure the data is properly filtered or encrypted. The anonymized communication record table contains data pertaining to at least one of telephone calls, messages, and data connectivity sessions that are initiated or received by multiple subscribers of a wireless telecommunication network. The anonymized communication record is generated from an original communication record table via filtering out or encryption of the data pertaining to one or more subscribers. The performance of the anonymization validation includes performing opt-out filtering validation, network cell anonymity filtering validation, and telephone number encryption validation on the anonymized communication record table.