The present disclosure is related to the network communication technology field and relates to a method for the classification and recognition of the Domain Name System (DNS) server, using machine-learning techniques. The classification process assigns a given DNS server as belonging to a preset of classes. For example, it enables to label a DNS server as either benign or malicious. On the other hand, the recognition process seeks the identification of the DNS server behavioral profile, which, consequently, can be used to assess the DNS server trustworthiness before DNS responses can be reliably used, e.g. identification of well-known and trusted DNS servers. Hence, the present patent, by the means of detecting the DNS server RFC adherence improves user security through the classification and recognition of DNS characteristics. Therefore, security solutions can use the DNS server characteristics to assess its trustworthiness before DNS responses can be reliably used.

Disclosed herein are systems, methods, and software for managing bot detection in a content delivery network (CDN). In one implementation, a cache node in a CDN may obtain a content request without a valid token for content not cached on the cache node and, in response to the content request, generate a synthetic response for the content request, wherein the synthetic response comprises a request for additional information from the end user device associated with the content request. The cache node further may obtain a response from the end user device and determine whether to satisfy the request based on whether the response from the end user device indicates that it is a bot.

Examples include a method of predictive rate limiting for performing services requested by a client in a cloud computing system. The method includes receiving a request from a client for one of a plurality of services to be performed, the client belonging to an organization; and determining a current threshold for the organization by applying a real time data model and a historical data model, the real time data model generating a first threshold at least in part by determining a number of requests received from the organization over a first preceding period of time; the historical data model generating a second threshold, the historical data model being generated by applying a machine learning model to historical data stored during processing of previous requests for the plurality of services from the organization over a second preceding period of time, the current threshold being the average of the first threshold and the second threshold. The method further includes performing the requested service when the current threshold is not exceeded; and denying the request when the current threshold is exceeded.

In one embodiment, a device in a network receives an attack mitigation request regarding traffic in the network. The device causes an assessment of the traffic, in response to the attack mitigation request. The device determines that an attack detector associated with the attack mitigation request incorrectly assessed the traffic, based on the assessment of the traffic. The device causes an update to an attack detection model of the attack detector, in response to determining that the attack detector incorrectly assessed the traffic.

Systems and methods for providing identification tests. In some embodiments, a system and a method are provided for generating and serving to a user an animated challenge graphic comprising a challenge character set whose appearance may change over time. In some embodiments, marketing content may be incorporated into a challenge message for use in an identification test. The marketing content may be accompanied by randomly selected content to increase a level of security of the identification test. In some embodiments, a challenge message for use in an identification test may be provided based on information regarding a transaction for which the identification test is administered. For example, the transaction information may include a user identifier such as an IP address. In some embodiments, identification test results may be tracked and analyzed to identify a pattern of behavior associated with a user identifier. A score indicative of a level of trustworthiness may be computed for the user identifier.

Systems, methods, apparatuses, and software for a content delivery network that caches content for delivery to end user devices is presented. In one example, a content delivery network (CDN) is presented having a plurality of cache nodes that cache content for delivery to end user devices. The CDN includes an anonymization node configured to establish anonymized network addresses for transfer of content to cache nodes from one or more origin servers that store the content before caching by the CDN. The anonymization node is configured to provide indications of relationships between the anonymized network addresses and the cache nodes to a routing node of the CDN. The routing node is configured to route the content transferred by the one or more origin servers responsive to content requests of the cache nodes based on the indications of the relationships between the anonymous network addresses to the cache nodes.

Identification of malicious network domains through use of links analysis of graph representation of network activity, such as a bipartite graphs. An example method includes setting an initial reputation score for each of a plurality of host computers and each of a plurality of domains accessed by the plurality of host computers; until a predefined condition is satisfied, iteratively rescoring the reputation scores for each of the plurality of host computers based upon the reputation scores of the plurality of domains; and rescoring the reputation scores for each of the plurality of domains based upon the reputation scores of the plurality of host computers; and determining, based upon the rescored reputation scores for each of the plurality of host computers and the rescored reputation scores for each of the plurality of domains, whether one or more domains amongst the plurality of domains are exhibiting malicious behavior.

A system and method identifies activity data that is related to activity of a plurality of users of a gaming platform. The activity data is used by the gaming platform to perform a gaming process. The system and method identifies first data of the activity data based on a first characteristic. The first data is a subset of the activity data. The system and method determines a number of times that the first data of the activity data meets a first condition. The system and method responsive to determining that the number of times that the first data of the activity data meets the first condition satisfies a first threshold, modifies the activity data by removing the first data from the activity data. The system and method performs the gaming process using the modified activity data.

Systems and methods for identifying, in a domain name, n-grams that do not appear in words of a given language, where n is greater than two are disclosed. The disclosed systems and methods may include comparing a value based on a number of the identified n-grams to a threshold and indicating that the domain name is potentially generated by malware in response to the value having a specified relationship with respect to the threshold.

Methods, non-transitory computer readable media, anomaly detection apparatuses, and network traffic management systems that generate, based on the application of one or more models and for a first flow associated with a received first set of network traffic, one or more likelihood scores and at least one flow score based on the likelihood scores. One or more of the one or more models are associated with one or more browsing patterns for a web application to which the first set of network traffic is directed. A determination is made when the flow score exceeds a threshold. A mitigation action is initiated, based on a stored policy, with respect to the first set of network traffic, when the determining indicates that the flow score exceeds the established threshold.