A malicious encrypted traffic inhibitor connected to a computer network is disclosed. A method for inhibiting malicious encrypted network traffic communicated via a computer network also is disclosed. The malicious encrypted traffic inhibitor and method utilize an estimated measure of entropy for a portion of network traffic communicated over a network connection via the computer network. The estimated measure of entropy is calculated as a measure of a degree of indeterminacy of information communicated via the network connection, such as an estimated measure of Shannon entropy, and then compared with a reference measure of entropy for malicious encrypted network traffic. If the estimated measure of entropy for traffic communicated via the computer network is sufficiently similar to the reference measure of entropy, a positive identification of malicious traffic on the computer network can be output.

Systems, methods, and apparatuses are provided for restricting access to a web resource. Website access information is obtained by monitoring accesses to a plurality of websites for each access, which may include a network identifier of an access requestor, a website identifier, and an access time for each request. Based on at least the website access information, it may be determined that a particular access requestor has accessed a number of different websites in a given time period. As a result, the particular access requestor may be classified as a web robot. A request to permit access to a web resource is received by the particular access requestor. In response to receiving the request to permit access to the web resource, the particular access requestor is prevented from accessing the web resource and/or a notification is generated that the particular access requestor is attempting to access the web resource.

A detection device includes a data-propagation tracking unit that gives communication data a tag including attribute information associated with communication destination information of the communication data and tracks propagation of communication data on which the tag including the attribute information is given, and a falsification detection unit that detects falsification on the communication data when, in the communication data, there is a tag including attribute information different from attribute information corresponding to a transmission destination or a transmission source of the communication data.

In one aspect, a computerized method useful for a detecting a data-center bot interacting with a web page includes the step of inserting a code within web page source. The computerized method includes the step of detecting that the web page is visited by a machine, wherein the machine is running a web browser to access the web page. The computerized method includes the step of rendering and loading the web page with the code in the web browser of the machine. The computerized method includes the step of, with the code, creating a hidden canvas element.

An approach for high-volume network threat tracing and detection may be implemented by storing network communications received from a plurality of hosts in an initial recording data structure, such as a rolling buffer. Identifiers may be generated for the plurality of hosts associated with the network communications by according to host identity or the behavior of a given host. Extended trace time values may be assigned to a portion of the plurality of hosts based at least in part on the identifiers, and storing the portion of the network communications that have extended trace time values may be recorded as packet capture files in long term memory.

A method is provided for preventing an IoT device within a trusted system from being harnessed in a malicious DDOS attack. The method may include bombarding the IoT device. The bombardment may originate from within the system, and may inundate the IoT device with harmless packets in a manner mimicking a traditional DOS attack. The inundating may utilize the resources of the IoT device to respond to the bombardment, and may thereby render the IoT device unavailable for fraudulent uses.

An identifying device monitors malware to be analyzed and acquires, as log data, the malware, download data downloaded from a communication destination, and a relation of data transfer performed with the malware or the communication destination of the download data. Then, the identifying device creates, by using the acquired log data, a dependency relation graph that is a digraph in which the malware, download data, and communication destination are set as nodes and a dependency relation of each node is set as an edge. Then, the identifying device detects a malicious node by collating the respective nodes of the created dependency relation graph with the known maliciousness information, and traces an edge in a direction from a terminal point to a start point while setting the malicious node as a base point, and then identifies the traced node as a new malicious node.

In one embodiment, a device including a processor, and a memory to store data used by the processor, wherein the processor is operative to run a manufacturer usage description (MUD) controller operative to obtain a MUD profile of an Internet of Things (IoT) device from a MUD server, the MUD profile of the IoT device including: access rights of the IoT device, and any one or more of the following a default device username and/or a default device password of the IoT device, a recommended/required device password complexity of the IoT device, at least one service that should be enabled/disabled on the IoT device, and/or allowed security protocols and/or ciphers for communication to and/or from the IoT device, enforce security of the IoT device according to the MUD profile of the IoT device. Related apparatus and methods are also described.

In one embodiment, a centralized controller maintains a plurality of hierarchical behavioral modules of a behavioral model, and distributes initial behavioral modules to data plane entities to cause them to apply the initial behavioral modules to data plane traffic. The centralized controller may then receive data from a particular data plane entity based on its having applied the initial behavioral modules to its data plane traffic. The centralized controller then distributes subsequent behavioral modules to the particular data plane entity to cause it to apply the subsequent behavioral modules to the data plane traffic, the subsequent behavioral modules selected based on the previously received data from the particular data plane entity. The centralized controller may then iteratively receive data from the particular data plane entity and distribute subsequently selected behavioral modules until an attack determination is made on the data plane traffic of the particular data plane entity.

Bots are detected real-time by correlating activity between users using a lag-sensitive hashing technique that captures warping-invariant correlation. Correlated users groups in social media may be found that represent bot behavior with thousands of bot accounts detected in a couple of hours.