BlackBerry Limited

A method of obtaining vehicle signal data. The method comprises reading a vehicle signals catalog by a communication service layer application that executes on a computer system, wherein the catalog defines vehicle signals as full path filenames; receiving a request for a value of a vehicle signal by the communication service layer application from a second application, wherein the request identifies the vehicle signal as a full path filename; obtaining the value of the vehicle signal by the communication service layer application; and returning the value of the vehicle signal the second application.

Devices, systems, and operation methods of a device is provided. In one aspect, the device includes an external assembly including an external antenna and an internal assembly. The internal assembly includes an internal antenna, a transceiver, and a radio frequency (RF) switch coupled to the transceiver, the internal antenna and the external antenna. The RF switch is configured to selectively couple at least one of the external antenna or the internal antenna to the transceiver. The external antenna is coupled to the RF switch through at least one conductor.

Systems, methods, and software can be used to detect a malware file. In some aspects, a method includes: obtaining features from a binary file to be classified as either a malware file or a non-malware file; inputting the obtained features to a first trained machine learning model; outputting, by the first trained machine learning model, an encoded vector; inputting the encoded vector to a second trained machine learning model, the second trained machine learning model generating text as output; and outputting, by the second trained machine learning model, a human-understandable text explanation of malicious activities that can be performed by the binary file, the human-understandable text explanation being generated without executing the binary file, the text explanation enabling a classification of the binary file.

It is proposed a method comprising obtaining, for identified processes from raw data, a list of activities and a list of used resources related to an execution of identified processes; generating, for each of the identified processes, a graph representation, wherein the graph representation comprises nodes and edges, wherein the nodes are assigned with elements from the list of used resources or an identifying value of one of the two identified processes, and wherein the edges are assigned with elements from the list of activities; combining the generated graph representations of the identified processes when the identified processes are linked together, wherein the combining outputting a combined graph representation being a representation of one of the events to be classified; providing the representation of one of the events to be classified to a classification model.

It is proposed a method for stopping or letting a process associated with an on-going event on a device by using a graph representation of the process, a prediction of the actions of the process to be performed on resources of the device by using a first trained model that outputs a predicted graph representation of the process, and a second trained model that delivers a score representative of the dangerousness of the process, the second trained model using the predicted graph representation of the process.

Methods and systems for controlling an asset tracking device and, in particular, determining when an asset tracking device is aboard a ship. The device may have a ship mode in which the device determines it has been loaded onto a ship and in which geolocation is not determined. The device may further have an ocean mode to which it transitions from the ship mode in which it ceases to look for a cellular connection in order to send location reports. The device may detect a beacon signal from a ship-mounted base-station to trigger the ship mode and may detect, or fail to detect, one or more wireless signals or identifiers as the trigger to transition to ocean mode.

In some examples, a sensor service receives an indication of interest from a client for sensor data of a first sensor of the plurality of sensors, and allocates buffers in the memory for the plurality of sensors. The sensor service provides a first buffer to a sensor connector that is to receive the sensor data from the first sensor, and receives, from the sensor connector, an indication that the first buffer in the memory has been written with the sensor data from the first sensor. Based on the indication of interest from the client, the sensor service notifies the client that the first buffer is available for reading by the client from the memory.

Systems, methods, and software can be used to detect anomalies in source code. In some aspects, a method includes obtaining, by a server, one or more attribute values associated with one or more code commits of source code. The server generates a backdoor abstraction of the source code, where the backdoor abstraction includes a structured representation of security backdoor elements extracted from the source code. The server generates, based on the one or more attribute values and the backdoor abstraction of the source code, an anomaly report indicating a risk level of the source code.

Systems, methods, and software can be used to detect backdoors in binary software code. In some aspects, a method comprises: obtaining, by a server, binary software code corresponding to source code; generating, by the server, a backdoor abstraction of the binary software code; and generating, by the server, a backdoor risk assessment based on the backdoor abstraction of the binary software code.

A method at a computing device within an Intelligent Transportation System, the method including detecting a traffic event at the computing device; determining no fixed roadside unit is available from the computing device; establishing an Internet Protocol connection from the computing device to a network node; and sending a traffic event message from the computing device to the network node, the traffic event message providing information for the traffic event.