Accenture Global Solutions Limited

Finbarr S Tarrant

Karel Kohout

Navdeep Sharma

Richard Levine

Ganesh Devarajan

Sarah Healy

Perikles Rammos

Eoghan Kidney

Mário Lauande Lacroix

Sidath Handurukande

Owen McGauley

Shane O Meachair

Jee Hyub Kim

Cian O'Hagan

Gopal Kavanadala Sridhar

Johnny Obando

David Glyn Darkin

Mehdi Sanoh

Eanna Mulrooney

Anton Plotnikov

Shannon Power

Richard Mcniff

Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their triage based on automated decision-tree triage and/or machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and automatically determine whether the potential vulnerability issues are exploitable using automated triage policies containing decision trees or by extracting vulnerability features from the report and processing the extracted vulnerability features using machine learning models.

Systems and methods for triaging software vulnerabilities

Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their automated triage based on machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and extract features from the report for each potential vulnerability issue. The system may receive policy data and business rules, and compare the extracted features relative to such data and rules. The system may determine a token based on the source code of a potential vulnerability issue, and a vector based on the extracted features of a potential vulnerability issue and based on the token. The system may select a machine learning modelling method and/or an automated triaging method based on the vector, and determine a vulnerability accuracy score based on the vector using the selected method.

Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Real-time motion feedback for extended reality

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Dublin, Ireland

Finbarr S Tarrant