A method for tracking machines on a network of computers includes determining one or more assertions to be monitored by a first web site which is coupled to a network of computers. The method monitors traffic flowing to the web site through the network of computers and identifies the one or more assertions from the traffic coupled to the network of computers to determine a malicious host coupled to the network of computers. The method includes associating a first IP address and first hardware finger print to the assertions of the malicious host and storing information associated with the malicious host in one or more memories of a database. The method also includes identifying an unknown host from a second web site, determining a second IP address and second hardware finger print with the unknown host, and determining if the unknown host is the malicious host.

A botnet identification module identifies members of one or more botnets based upon network traffic destined to one or more servers over time, and provides sets of botnet sources to a traffic monitoring module. Each set of botnet sources includes a plurality of source identifiers of end stations acting as part of a corresponding botnet. A traffic monitoring module receives the sets of botnet sources from the botnet identification module, and upon a receipt of traffic identified as malicious that was sent by a source identified within one of the sets of botnet sources, activates a protection mechanism with regard to all traffic from all of the sources identified by the one of the sets of botnet sources for an amount of time.

Certain aspects and features of the present disclosure relate to systems and methods for automatically classifying hosts in real-time. For instance, classifying hosts as bots, and subsequently mitigating or blocking traffic from the hosts classified as bots can be advantageous in real-time data exchange systems. In a real-time data exchange system, data can be exchanged between a server and a target host in real-time when the target host accesses a webpage. Inhibiting data communication between servers and hosts operated by bot scripts can reduce fraudulent activity. In some implementations, hosts can be automatically classified into various groups based at least in part on the data included in requests received from the hosts.

Systems and methods for identifying a weak stimulus in a stimulus-based authentication system is provided. Counters are associated with each stimulus used in the authentication and a first counter is incremented when the stimulus is used in an authentication session and a second counter is incremented when a successful event occurs with respect to the stimulus during the authentication session, but the authentication session ultimately fails. A ratio of the second counter and the first counter is compared to a threshold and the stimulus is identified as weak when the ratio exceeds the threshold. The stimulus may then be removed and no longer be used in the stimulus-based authentication system.

A server interacts with a bot detection service to provide bot detection as a requesting client interacts with the server. In an asynchronous mode, the server injects into a page a data collection script configured to record interactions at the requesting client, to collect sensor data about the interactions, and to send the collected sensor data to the server. After the client receives the page, the sensor data is collected and forwarded to the server through a series of posts. The server forwards the posts to the detection service. During this data collection, the server also may receive a request from the client for a protected endpoint. When this occurs, and in a synchronous mode, the server issues a query to the detection service to obtain a threat score based in part on the collected sensor data that has been received and forwarded by the server. Based on the threat score returned, the server then determines whether the request for the endpoint should be forwarded onward for handling.

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.

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 detection device includes processing circuitry configured to collect communication information in a network including clients and servers, generate a matrix representing states of access from the clients to the servers using the communication information collected, aggregate a plurality of the clients accessing a target server and generate statistical information of similiarities between the aggregated clients in the matrix as a feature amount of the target server, learn, with regard to the target server which is a server for which it is known whether the server is a malicious server, a model for determining whether a server is a malicious server using the feature amount generated, and determine, with regard to the target server which is a server for which it is unknown whether the server is a malicious server, whether the target server is a malicious server using the feature amount generated and the model.

A computer system configured to improve security of server computers interacting with client computers, the system comprising: one or more processors executing instructions that cause the one or more processors to: select, from the plurality of detection tests, one or more first detection tests to be performed by a client computer; send, to the client computer, a first set of detection instructions that define the one or more first detection tests, and which when executed causes generating a first set of results that identifies a first set of characteristics of the client computer; receive the first set of results from the client computer; select one or more first countermeasures from a plurality of countermeasures based on the first set of characteristics identified in the first set of results; send, to the client computer, a first set of countermeasure instructions that define the one or more first countermeasures.

Systems and methods for dynamically mitigating a DDOS attack. In an aspect, the technology relates to a computer-implemented method for dynamically mitigating a distributed-denial-of-service (DDOS) attack. The computer-implemented method may include detecting a DDOS attack directing malicious traffic to a target, identifying one or more source locations of the malicious traffic, and in response to detecting the DDOS attack, activating one or more scrub clusters in the identified one or more source locations of the malicious traffic. The method may further include directing traffic intended for the target to the to the activated one or more scrub clusters, detecting an end of the DDOS attack, and in response to detecting the end of the DDOS attack, deactivating the one or more scrub clusters to release hardware resources.