Ut-battelle, Inc.

Joel A Dawson

Ali Passian

Milton Nance Ericson

Michael D Iannacone

Robert C Jordan

Varisara Tansakul

Joel M Asiamah

Srikanth B Yoginath

Gavin B Long

Additive manufacturing's reliance on embedded computing renders it vulnerable to tampering through cyber-attacks. Sensor instrumentation of additive manufacturing devices allows for rigorous process and security monitoring, but also results in a massive volume of noisy data for each run. As such, in-situ, near-real-time anomaly detection is challenging. A probabilistic-model-based approach addresses this challenge.

Stealthy process attack detection for automated manufacturing

A system and method (referred to as the system) detect infectious code. The system injects a repetitive software code that causes malware in a monitored device to render a detectable direct current power consumption profile. A guide wave generator generates a guide wave signal that establishes an observational window that is applied to data that represent a direct current source power consumption of the monitored device. An extraction device extracts a portion of the data that represent the direct current source power consumption of the monitored device. A deviation engine identifies the malware on the monitored device without processing data associated with a prior identification of the malware or identifying a source of the malware or identifying a location of the malware on the monitored device.

Rootkit detection system

Growing community of inventors

Oak Ridge, TN, United States of America

Joel A Dawson