A threat monitoring and vulnerability management system is disclosed. The system includes one or more sensors configured to scan a frequency spectrum of a project 25 (P25) network and to collect data on the P25 network. The system further includes a server coupled to the sensors and configured to receive the collected data from the plurality of sensors, compare the collected data with previously stored historical data to determine whether an anomaly exists within data patterns of the collected data, responsive to determining that the anomaly exists, determine at least one of: whether use of a cloned radio that mimics an authorized connection occurs, whether jamming of a radio frequency (RF) communication occurs, or whether jamming of a voice communication occurs within the P25 network by comparing the collected data with preset thresholds, and send a real-time alert to a dispatch and control console unit coupled to the server and the P25 network in response to determining that some of the collected data exceeds at least one of the preset thresholds, such that the dispatch and control console unit provides one or more corrective actions to the P25 network.

In one embodiment, a device maintains a buffer of historical telemetry data of a particular type of telemetry. The device obtains new telemetry data of the particular type of telemetry. The device makes a state evaluation by comparing the new telemetry data to the buffer, to determine whether the new telemetry data is an outlier. The device sends a message indicative of the new telemetry data to a message bus for delivery to a recipient that is not subscribed to receive telemetry data of the particular type of telemetry, when the device determines that the new telemetry data is an outlier.

A cloud network is a complex environment in which hundreds and thousands of users or entities can each host, create, modify, and develop multiple virtual machines. Each virtual machine can have complex behavior unknown to the provider or maintainer of the cloud. Technologies disclosed include methods, systems, and apparatuses to monitor the complex environment to detect network anomalies using machine learning techniques. In addition, techniques to modify and adapt to user feedback are provided allowing the developed models to be tuned for specific use cases, virtual machine types, and users.

Systems, methods and computer-readable storage media are utilized to analyze multi-channel data based on a security model in a computer network environment. A computing system is communicatively coupled to a plurality of data channels configured to access entity data via at least one data channel communication network. A plurality of data sources configured to store entity data are associated with the respective data channels. A processing circuit is communicatively coupled to a particular data channel via a data channel communication network and is structured to receive, via the data channel, entity data comprising device connectivity data, parse properties from the device connectivity data where the properties correspond to particular security dimensions, identify vulnerabilities associated with the properties, determine vulnerability impact, and generate a multi-dimensional risk score for a target computer network environment associated with the entity.

Circuits and methods are provided for detecting, identifying and/or removing undesired content. According to one embodiment, a method for performing content scanning of content objects is provided. A content object that is to be scanned is stored by a general purpose processor to a system memory of the general purpose processor. Content scanning parameters associated with the content object are set up by the general purpose processor. Instructions from a signature memory of a co-processor that is coupled to the general purpose processor are read by the co-processor based on the content scanning parameters. The instructions contain op-codes of a first instruction type and op-codes of a second instruction type. Those of the instructions containing op-codes of the first instruction type are assigned by the co-processor to a first instruction pipe of multiple instruction pipes of the co-processor for execution. An instruction of the assigned instructions containing op-codes of the first instruction type is executed by the first instruction pipe including accessing a portion of the content object from the system memory.

Security system for protecting a vehicle electronic system by selectively intervening in the communications path in order to prevent the arrival of malicious messages at ECUs, in particular at the safety critical ECUs. The security system includes a filter which prevents illegal messages sent by any system or device communicating over a vehicle communications bus from reaching their destination. The filter may, at its discretion according to preconfigured rules, send messages as is, block messages, change the content of the messages, request authentication or limit the rate such messages can be delivered, by buffering the messages and sending them only in preconfigured intervals.

A proxy server in a cloud-based proxy service receives a message that indicates that a domain, whose traffic passes through the proxy server, may be under a denial-of-service (DoS) attack. The proxy server enables a rule for the domain that specifies that future requests for resources at that domain are subject to at least initially passing a set of one or more challenges. In response to receiving a request for a resource of that domain from a visitor, the proxy server presents the set of challenges that, if not passed, are an indication that that the visitor is part of the DoS attack. If the set of challenges are passed, the request may be processed. If the set of challenges are not passed, the request may be dropped.

The present disclosure describes systems and methods for detecting malware. More particularly, the system includes a monitoring device that monitors side-channel activity of a target device. The monitoring device that can work in conjunction with (or independently of) a cloud-based security analytics engine to perform anomaly detection and classification on the side-channel activity. For example, the monitoring device can calculate a first set of features that are then transmitted to the security analytics engine for anomaly detection and classification.

Example embodiments disclosed herein relate to monitoring Dynamic Device Configuration Protocol offers via a control plane. In one example, an address range or multiple address ranges for sources of the Dynamic Device Configuration Protocol offers can be tracked. In this example, an anomaly can be determined based on one of the Dynamic Device Configuration Protocol offers and the address range(s).

Disclosed herein are a dynamic security module server device for transmitting a dynamic security module to a user terminal and receiving a security management event from the user terminal, and a method of operating the dynamic security module server device. The dynamic security module server device includes a communication unit configured to transmit and receive a security management event over a network, and a processor configured to control the communication unit. The processor is configured to create a security session with the security client of a user terminal, and to transmit a dynamic security module to the security client of the user terminal so that part or all of code performing security management in the security client of the user terminal in which the security session has been created has a predetermined valid period.