British Telecommunications Public Limited Company

Robert Hercock

Giulio Giaconi

Jonathan Roscoe

A computer implemented method of clustering computer systems in a plurality of systems to identify computer systems being subject to a common security occurrence, each computer system generating data records corresponding to security events in respect the systems, the method comprising: receiving a set of one or more data records associated with each computer system, each record including a sequence of data fields; generate a vector embedding for each data field in each record; evaluate a distance of each vector embedding from a reference vector as an indicator of semantic distance; identifying similar data records based on a measure of a degree of similarity of the distances of vector embeddings for each record; defining a cluster of computer systems including computer systems associated with the similar data records for applying protective measures to the computer systems in the cluster.

Identifying relationships in data

A computer implemented method of feature detection in temporal graph data structures of events, the method including receiving a temporal series of graph data structures of events each including a plurality of nodes corresponding to events and edges connecting nodes corresponding to relationships between events; rendering each graph data structure in the series as an image representation of the graph data structure including a representation of nodes and edges in the graph being rendered reproducibly in a cartesian space based on attributes of the nodes and edges, so as to generate a temporal series of image representations ordered according to the temporal graph data structures; processing the series of image representations by a convolutional neural network to classify the image series so as to identify a feature in the image series, the convolutional neural network being trained by a supervised training method including a plurality of training example image series in which a subset of the training examples are classified as including the feature.

Feature detection with neural network classification of images representations of temporal graphs

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.

Detecting vulnerable software systems

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 aggregate vector representation for the software system so adjusted to compare with the multiple generations of aggregate vector representations to identify a software component adjustment leading to a change in vector representation not exceeding the maximum threshold degree of change so as to reduce the vulnerability of the software system.

Remediating software vulnerabilities

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 change as an indication of an increased vulnerability of the software system.

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London, United Kingdom

Robert Hercock