Data stored on a data asset may be migrated to another data asset while maintaining compliance to applicable regulations. A data asset may experience a failure. Based on the type of data stored by that data asset and the applicable regulations, requirements, and/or restrictions that relate to a transfer of that type data from that data asset, a target data asset may be determined. The data stored on the data asset may then be transferred to the target data asset. The disclosed systems may use data models and/or data maps in determining the requirements for a data transfer and selecting target data assets.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

A method for transmitting data in a computer network is provided, which comprises, at a first node of the network: receiving a computing puzzle from a puzzle server node of the network distinct from the first node; determining a solution to the puzzle for transmitting a message to a second node of the network distinct from the puzzle server node; and transmitting data to the second node, wherein the transmitted data comprises a message and the determined solution to the puzzle.

A system and computer-implemented method of managing botnet attacks to a computer network is provided. The system and method includes receiving a DNS request included in network traffic, each DNS request included in the network traffic and including a domain name of a target host and identifying a source address of a source host, wherein the translation of the domain name, if translated, provides an IP address to the source host that requested the translation. The domain name of the DNS request is compared to a botnet domain repository, wherein the botnet domain repository includes one or more entries, each entry having a confirmation indicator that indicates whether the entry corresponds to a confirmed botnet. If determined by the comparison that the domain name of the DNS request is included in the botnet domain repository, then the source address of the DNS request is stored or updated in an infected host repository and a control signal is output to cause any future network traffic from the source address to be diverted to an administrator configured address. Each source address stored in the infected host repository identifies a host known to be infected.

A method and a system for detecting an unauthorized activity at a user device are provided. The method comprises: analyzing a first request from the user device, the first request including original client cookie; in response to the original client cookie meeting a predetermined threshold: causing the user device to receive a Java Script Module, thereby enabling the user device to generate a second request, by: receiving server cookie indicative of a given activity associated with the user device being one of: a user activity and a bot activity; generating the second request including first client cookie and the server cookie; determining if the second request is to be transmitted to a web content server associated with the first web page; in response to the server cookie data being indicative of the bot activity: the second request is blocked.

Methods and systems are provided for identifying suspect Internet Protocol (IP) addresses, in accordance with embodiments described herein. In particular, embodiments described herein include obtaining a set of login pairs comprising login identifiers (e.g., user identifiers) and IP addresses used in attempts to login to a source. A set of IP clusters is generated using the set of login pairs. Each IP cluster can include one or more IP addresses identified as related based on a login identifier being used to attempt to login to the source via multiple IP addresses or an IP address being used to attempt to login to the source via multiple login identifiers. Thereafter, it is determined that a particular IP cluster exceeds a threshold amount of IP addresses. Each of the IP addresses within the particular IP cluster is designated as a suspect IP address.

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.

Web traffic at different geographic traffic distribution buckets are compared against each other to try and machine-learn the underlying traffic parameters of legitimate (human-initiated) traffic. Distributions of the traffic parameters for the web traffic at multiple servers are compared to see whether they match. If so, matching or substantially matching traffic parameters signal that such web traffic is, in fact, legitimate. A clean profile is built with the matching traffic parameters and used to determine how much bot traffic is resident in web traffic at different servers.

This specification generally relates to methods and systems for applying network policies to devices based on their current access network. One example method includes identifying a proxy connection request sent from a particular client device to a proxy server over a network, the proxy connection request including a hostname and configured to direct the proxy server to establish communication with the computer identified by the hostname on behalf of the client device; determining an identity of the client device based on the proxy connection request; identifying a domain name system (DNS) response to a DNS request including the hostname from the proxy connection request; and updating DNS usage information for the particular client based on the identified DNS response including the hostname from the proxy connection request.

A method of detecting bots, preferably in an operating environment supported by a content delivery network (CDN) that comprises a shared infrastructure of distributed edge servers from which CDN customer content is delivered to requesting end users (clients). The method begins as clients interact with the edge servers. As such interactions occur, transaction data is collected. The transaction data is mined against a set of “primitive” or “compound” features sets to generate a database of information. In particular, preferably the database comprises one or more data structures, wherein a given data structure associates a feature value with its relative percentage occurrence across the collected transaction data. Thereafter, and upon receipt of a new transaction request, primitive or compound feature set data derived from the new transaction request are compared against the database. Based on the comparison, an end user client associated with the new transaction request is then characterized, e.g., as being associated with a human user, or a bot.