Abstract: A computer implemented method of remediating an increased vulnerability of a software system including a plurality of software components, the method including generating a vector representation of each software component derived from a neural network trained using training data defined from known vulnerabilities of the software components in the software system; aggregating the vector representations for the software component to an aggregate vector representation for a particular time; repeating the generating and the aggregating for a plurality of points in time to generate multiple generations of aggregate vector representations; comparing the multiple generations of aggregate vector representations to detect a change in an aggregate vector representation exceeding a maximum threshold degree of change as an indication of an increased vulnerability of the software system, responsive to which iteratively adjusting the software components in the software system and, at each iteration, regenerating an aggrega

Abstract: A computer implemented method of detecting an increased vulnerability of a software system including a plurality of software components, the method including generating a vector representation of each software component derived from a neural network trained using training data defined from known vulnerabilities of the software components in the software system; aggregating the vector representations for the software component to an aggregate vector representation for a particular time; repeating the generating and the aggregating for a plurality of points in time to generate multiple generations of aggregate vector representations; and comparing the multiple generations of aggregate vector representations to detect a change in an aggregate vector representation exceeding a maximum threshold degree of change as an indication of an increased vulnerability of the software system.

Abstract: A computer implemented method of detecting anomalous behavior within a computer network, the method including accessing data records each corresponding to an occurrence of communication occurring via the computer network and including a plurality of attributes of the communication; generating, for each of at least a subset of the data records, a training data item for a neural network, the training data item being derived from at least a portion of the attributes of the record and the neural network having input units and output units corresponding to items in a corpus of attribute values for communications occurring via the network; augmenting the training data by replicating each of one or more training data items responsive to one or more attributes of the data record corresponding to the training data item; training the neural network using the augmented training data so as to define a vector representation for each attribute value in the corpus based on weights in the neural network for an input unit cor

Abstract: A computer implemented method of detecting an increased vulnerability of a software system including a plurality of software components, the method including generating a vector representation of each software component derived from a neural network trained using training data defined from known vulnerabilities of the software components in the software system, wherein the training data is augmented by replicating each of one or more training data items in the training data responsive to one or more attributes of a vulnerability corresponding to the training data item; aggregating the vector representations for the software component to an aggregate vector representation for a particular time; repeating the generating and the aggregating for a plurality of points in time to generate multiple generations of aggregate vector representations; comparing the multiple generations of aggregate vector representations to detect a change in an aggregate vector representation exceeding a maximum threshold degree of chan

Abstract: A device identification method, a device identification system and a device prediction component. The method can include determining, based on first power consumption data indicative of a first power consumption associated with a premises within a first time period, a predicted identity of an active device at the premises within a second time period subsequent to the first time period. A detected identity of the active device at the premises within the second time period is determined, based on second power consumption data indicative of a second power consumption associated with the premises within the second time period. A determined identity of the active device at the premises within the second time period is determined, based on at least one of the predicted identity and the detected identity.

Abstract: A method of identifying anomalous network activity. The method includes identifying, based on network data representative of network activity within a network, at least one instance of a sequence of events that occurred within the network. A probability of the sequence of events occurring during non-anomalous network activity is obtained based on transition probabilities between events in the sequence of events. A frequency characteristic dependent on a frequency at which the sequence of events occurred within the network is determined. A likelihood of the sequence of events occurring within the network at the frequency is determined based on a combination of the probability and the frequency characteristic. It is identified, based on the likelihood, that at least a portion of the network data is anomalous.

Abstract: A method of identifying anomalous data obtained by at least one sensor of a plurality of sensors located within an environment. The method includes identifying, based on sensor data obtained from the plurality of sensors, at least one instance of a sequence of events that occurred within the environment. A probability of the sequence of events occurring within the environment under non-anomalous conditions is obtained. A frequency characteristic dependent on a frequency at which the sequence of events occurred within the environment is determined. A likelihood of the sequence of events occurring within the environment at the frequency is determined, based on a combination of the probability and the frequency characteristic. It is identified, based on the likelihood, that at least a portion of the sensor data is anomalous.

Abstract: A computer implemented method for detecting anomalies in a computer network is provided together with a network monitoring system and computer programs for carrying out the method. The method obtains a model representing normal characteristics of network traffic associated with a set of devices within the computer network. The method analyses network traffic using the model to identify anomalous network traffic associated with the set of devices. The method clusters the anomalous network traffic into clusters of network traffic that share similar characteristics. The method provides an indication that either (i) the network traffic associated with a cluster relates to a new type of anomaly involving the set of devices or (ii) that no new types of anomaly are present.

Abstract: A computer implemented method of detecting anomalous behavior in a set of computer systems communicating via a computer network, the method including evaluating a difference in a level of activity of the computer system between a baseline time period and a runtime time period, and responsive to a determination of anomalous behavior, implementing one or more protective measures for the computer network.

Abstract: A computer implemented method of detecting anomalous behavior within a computer network, the method including accessing data records each corresponding to an occurrence of communication occurring via the computer network and including a plurality of attributes of the communication; generating, for each of at least a subset of the data records, a training data item for a neural network, the training data item being derived from at least a portion of the attributes of the record and the neural network having input units and output units corresponding to items in a corpus of attribute values for communications occurring via the network; augmenting the training data by replicating each of one or more training data items responsive to one or more attributes of the data record corresponding to the training data item; training the neural network using the augmented training data so as to define a vector representation for each attribute value in the corpus based on weights in the neural network for an input unit cor

Abstract: A computer implemented method of detecting an increased vulnerability of a software system including a plurality of software components, the method including generating a vector representation of each software component derived from a neural network trained using training data defined from known vulnerabilities of the software components in the software system; aggregating the vector representations for the software component to an aggregate vector representation for a particular time; repeating the generating and the aggregating for a plurality of points in time to generate multiple generations of aggregate vector representations; and comparing the multiple generations of aggregate vector representations to detect a change in an aggregate vector representation exceeding a maximum threshold degree of change as an indication of an increased vulnerability of the software system.

Abstract: A computer implemented method of remediating an increased vulnerability of a software system including a plurality of software components, the method including generating a vector representation of each software component derived from a neural network trained using training data defined from known vulnerabilities of the software components in the software system; aggregating the vector representations for the software component to an aggregate vector representation for a particular time; repeating the generating and the aggregating for a plurality of points in time to generate multiple generations of aggregate vector representations; comparing the multiple generations of aggregate vector representations to detect a change in an aggregate vector representation exceeding a maximum threshold degree of change as an indication of an increased vulnerability of the software system, responsive to which iteratively adjusting the software components in the software system and, at each iteration, regenerating an aggrega

Abstract: A computer implemented method of detecting an increased vulnerability of a software system including a plurality of software components, the method including generating a vector representation of each software component derived from a neural network trained using training data defined from known vulnerabilities of the software components in the software system, wherein the training data is augmented by replicating each of one or more training data items in the training data responsive to one or more attributes of a vulnerability corresponding to the training data item; aggregating the vector representations for the software component to an aggregate vector representation for a particular time; repeating the generating and the aggregating for a plurality of points in time to generate multiple generations of aggregate vector representations; comparing the multiple generations of aggregate vector representations to detect a change in an aggregate vector representation exceeding a maximum threshold degree of chan

Abstract: A computer implemented method of detecting anomalous behavior in a set of computer systems communicating via a computer network, the method including evaluating a difference in a level of activity of the computer system between a baseline time period and a runtime time period, and responsive to a determination of anomalous behavior, implementing one or more protective measures for the computer network.