The present invention generally relates to a context-aware security self-assessment method or module that determines the context in which the device is used and based on this, assesses the devices security settings. The context may refer to the system environment, the applications the device is used for, and/or the current life-cycle stage of the device, without being limited to said contexts. The method of the present invention preferably prioritizes and rates the security relevant findings and presents them in combination with mitigation options through a web interface, a configuration tool, or through notifications in the control system.

A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The robot can patrol one or more routes within a building, and can detect violations of security policies by objects, building infrastructure and security systems, or individuals. In response to the detected violations, the robot can perform one or more security operations. The robot can include a removable fabric panel, enabling sensors within the robot body to capture signals that propagate through the fabric. In addition, the robot can scan RFID tags of objects within an area, for instance coupled to store inventory. Likewise, the robot can generate or update one or more semantic maps for use by the robot in navigating an area and for measuring compliance with security policies.

The invention provides systems and methods for detection of anomaly events or error events within a process environment by implementing a hybrid of centralized classifier models and edge node based classifier models. The invention comprises (i) receiving a first set of information from a first set of field devices located within a process control environment, (ii) transmitting a first input vector generated based on the first set of information, to a first anomaly detector implemented within an edge node configured to provide gateway access to said process control environment, wherein said first anomaly detector implements a first classifier model, (iii) responsive to the first anomaly detector detecting a anomaly event based on the transmitted first input vector, transmitting a second input vector to a second anomaly detector implemented within a cloud based server, and (iv) generating a anomaly event alert responsive to at least one of the first anomaly detector and the second anomaly detector detecting an anomaly event.

A smart electronic device management system is a device that is utilized to manage and control electronic devices. The device includes a housing structure that may be mounted to a surface such as a wall. A video capture device provides a live video feed of the surrounding areas while at least one environmental sensor allows monitoring of conditions in the surrounding areas. A wireless communication module allows the device to be associated with an external computing device. Various electronic devices may be connected to the device through a plurality of electrical outlets on the housing structure. A control unit allows the device to monitor and manage electronic devices that are wirelessly connected to the device or connected through the plurality of electrical outlets. The control unit is configured to calculate a sprinkler schedule using data retrieved through the wireless communication module.

To commission an industrial automation control system, IACS, a computing device generates commands to automatically set or verify a security configuration of the IACS. The commands are generated by the computing device based on a machine-readable security baseline, and, optionally, based on a machine-readable configuration file of the IACS.

A surveillance system may comprise one or more computing devices and one or more robotic surveillance devices. The one or more computing devices may be configured to obtain video data captured by one or more cameras. The one or more computing devices may analyze the video data to determine whether there is any trigger event. In response to determining that there is a trigger event, the one or more computing device may determine an optimal robotic surveillance device among the one or more robotic surveillance devices based on the trigger event and provide an instruction to the optimal robotic surveillance device. The optimal robotic surveillance device may be configured to perform a responding action in response to receiving the instruction.

An industrial control system hardened against malicious activity monitors highly dynamic control data to develop a dynamic thumbprint that can be evaluated to detect deviations from normal behavior of a type that suggest tampering or other attacks. Evaluation of the dynamic thumbprint may employ a set of ranges defining normal operation and reflecting known patterns of interrelationship between dynamic variables.

Operation of an industrial asset control system may be simulated or monitored under various operating conditions to generate a set of operating results. Subsets of the operating results may be used to calculate a normalization function for each of a plurality of operating conditions. Streams of monitoring node signal values over time may be received that represent a current operation of the industrial asset control system. A threat detection platform may then dynamically calculate normalized monitoring node signal values based at least in part on a normalization function in the operating mode database. For each stream of normalized monitoring node signal values, a current monitoring node feature vector may be generated and compared with a corresponding decision boundary for that monitoring node, the decision boundary separating normal and abnormal states for that monitoring node. A threat alert signal may then be automatically transmitted based on results of said comparisons.

A smart electronic device management system is a device that is utilized to manage and control electronic devices. The device includes a housing structure that may be mounted to a surface such as a wall. A video capture device provides a live video feed of the surrounding areas while at least one environmental sensor allows monitoring of conditions in the surrounding areas. A wireless communication module allows the device to be associated with an external computing device. Various electronic devices may be connected to the device through a plurality of electrical outlets on the housing structure. A control unit allows the device to monitor and manage electronic devices that are wirelessly connected to the device or connected through the plurality of electrical outlets. The control unit is configured to calculate a sprinkler schedule using data retrieved through the wireless communication module.

Techniques for previewing an operator display of a process section in a process plant include presenting a process section on a user interface device, where the process section includes a user control for presenting and/or displaying another process section on the process plant display. In response to receiving user input indicative of a request to display a preview of the other process section via the user control, the user interface device presents the other process section while simultaneously presenting the process section in a preview mode. The process sections in the preview mode may be presented side-by-side, above and below each other, in separate display windows, etc.