Microsoft Technology Licensing, LLC

Manoj Ajith Prasad

Michael Nelson

Weizhu Chen

Woo Ram Lee

Ganapathy V Raman

Sparky Toews

Aarohi Arora

Santosh Vasant Bhosale

Aritra DattaGupta

Roger F Johnson

Will Qian

Vladyslav Bazyliak

Somak Mitra

Bryan Wilhelm

Benjamin Reich

Jack Wilson Stokes, Iii

Emre Mehmet Kiciman

Matthew Justice

Andrew Thomas Marshall

Michael J Nelson

The techniques disclosed herein enable systems to train machine learning models using benign augmentation to enabled resistance various data poisoning attacks. This is achieved by first training a machine learning model using an initial dataset that is trustworthy and originates from a known source. The initial dataset is then modified to include known attack triggers such as syntactic paraphrasing to generate an augmented dataset. The augmented dataset is then used to train a robust machine learning model based using the initially trained machine learning model. The resultant robust machine learning model is then enabled to detect and resist attacks captured by the augmented dataset. The robust machine learning model can be retrained using an untrusted dataset that includes various compromised inputs in conjunction with the augmented dataset. Retraining results in an updated robust machine learning model that can learn and resist various data poisoning attacks on the fly.

Adversarial training to minimize data poisoning attacks

Methods for configurable and proactive application diagnostics and recovery are performed by systems and devices. A diagnostics manager determines diagnostics packages corresponding to problems described in client device diagnostics requests. Session identifiers are generated and returned with diagnostics identifiers to clients which then provide the session identifiers and diagnostics identifiers to a service manager for session initiation. Diagnostics packages are located, retrieved, and provided back to the client by the service manager that invokes a client-side engine to execute diagnostics packages. Results are provided to the diagnostics system which determines additional packages to be executed by the engine during the same diagnostics session. Further, device-specific tokens are acquired by client devices which execute local diagnostic packages and acquire remote diagnostic packages for execution in the same session. Additionally, diagnostics packages provide their own user interfaces, and local packages are automatically executed upon invocation of help applications.

System and method for configurable and proactive application diagnostics and recovery

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Redmond, WA, United States of America

Manoj Ajith Prasad