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.

The present disclosure is directed to a method of identifying an infected network node. The method includes identifying a first network node as infected. The method includes collecting a first set of network data from the first network node including anomalous activities performed by the first network node. The method includes generating an anomalous behavior model using the first set of network data. The method includes collecting a second set of network data from a second network node including anomalous activities performed by the second network node. The method includes comparing the second set of data to the generated anomalous behavior model. The method includes determining, from the comparison, that a similarity between first characteristics and second characteristics exceeds a predefined threshold. The method includes ascertaining, based on the determination, the second network node as an infected network node.

Described are techniques for differentiating humans from bots. The techniques including a computer-implemented method comprising presenting a motion-based challenge-response instruction to a user via a user interface of a first device of a plurality of devices associated with the user and communicatively coupled to one another by a network, where the motion-based challenge-response instruction describes at least one motion that is performable by the user and detectable by at least one of the plurality of devices, and where the motion-based challenge-response instruction is configured to differentiate humans from bots. The method further comprises determining that device data from one or more of the plurality of devices matches the at least one motion. The method further comprises authenticating the first device in response to determining that the device data matches the at least one motion, where authenticating the first device indicates that the user is a human.

Disclosed is an improved method, system, and computer program product for detecting hosts and connections between hosts that are being used as relays by an actor to gain control of hosts in a network. It can further identify periods of time within the connection when the relay activities occurred. In some embodiments, the invention can also chain successive relays to identify the true source and true target of the relay.

This disclosure describes a technique to fingerprint TLS connection information to facilitate bot detection. The notion is referred to herein as “TLS fingerprinting.” Preferably, TLS fingerprinting herein comprises combining different parameters from the initial “Hello” packet send by the client. In one embodiment, the different parameters from the Hello packet that are to create the fingerprint (the “TLS signature”) are: record layer version, client version, ordered TLS extensions, ordered cipher list, ordered elliptic curve list, and ordered signature algorithms list. Preferably, the edge server persists the TLS signature for the duration of a session.

Example methods and systems for a computer system to perform security threat detection are described. In one example, a computer system may intercept an egress packet from a virtualized computing instance to pause forwarding of the egress packet towards a destination and obtain process information associated a process from which the egress packet originates. The computer system may initiate security analysis based on the process information. In response to determination that the process is a potential security threat based on the security analysis, the egress packet may be dropped, and a remediation action performed. Otherwise, the egress packet may be forwarded towards the destination.

A detection method for a malicious domain name in a domain name system (DNS) and a detection device are provided. The method includes: obtaining network connection data of an electronic device; capturing log data related to at least one domain name from the network connection data; analyzing the log data to generate at least one numerical feature related to the at least one domain name; inputting the at least one numerical feature into a multi-type prediction model, which includes a first data model and a second data model; and predicting whether a malicious domain name related to a malware or a phishing website exists in the at least one domain name by the multi-type prediction model according to the at least one numerical feature.

Systems and methods for adaptively identifying anomalous network communication traffic. The system includes a processor and a memory coupled to the processor. The memory includes processor-executable instructions that configure the processor to: obtain data associated with a sequence of network communication events; determine that the sequence of communication events is generated by a computing agent based on a symmetricity measure associated with the sequence of network communication events; generate a threat prediction value for the sequence of network communication events prior-generated by the computing agent based on a combination of the symmetricity measure and a randomness measure associated with the network communication events; and transmit a signal for communicating that the sequence of network communication events is a potential malicious sequence of network communication events based on the threat prediction value.

Various embodiments provide an approach to detect intrusion of connected IoT devices. In operation, features associated with behavioral attributes as well as volumetric attributes of network data patterns of different IoT devices is analyzed by means of statistical analysis to determine deviation from normal operation data traffic patterns to detect anomalous operations and possible intrusions. Data from multiple networks and devices is combined in the cloud to provide for improved base models for statistical analysis.

A computer system is provided. The computer system includes a memory, a network interface, and a processor coupled to the memory and the network interface. The processor is configured to receive a response to a request to verify whether an ostensible client of a service is actually a client or a bot, the response including an indicator of whether the ostensible client is a client or a bot; receive information descriptive of interoperations between the ostensible client and the service that are indicative of whether the ostensible client is a client or a bot; and train a plurality of machine learning classifiers using the information and the indicator to generate a next generation of the plurality of machine learning classifiers.