Nec Corporation

Chung Hwan Kim

Junghwan Rhee

Zhichun Li

Kangkook Jee

Haifeng Chen

LuAn Tang

Hui Joyce Zhang

Guofei Jiang

Zhenyu Wu

Zhengzhang Chen

Nipun Arora

Xiao Yu

Lauri Korts-Parn

Kyungtae Kim

Shuai Hao

Ziqiao Zhou

Adil Ahmad

Hongyu Li

A computer-implemented method for efficient and scalable enclave protection for machine learning (ML) programs includes tailoring at least one ML program to generate at least one tailored ML program for execution within at least one enclave, and executing the at least one tailored ML program within the at least one enclave.

Efficient and scalable enclave protection for machine learning programs

A method for implementing confidential machine learning with program compartmentalization includes implementing a development stage to design an ML program, including annotating source code of the ML program to generate an ML program annotation, performing program analysis based on the development stage, including compiling the source code of the ML program based on the ML program annotation, inserting binary code based on the program analysis, including inserting run-time code into a confidential part of the ML program and a non-confidential part of the ML program, and generating an ML model by executing the ML program with the inserted binary code to protect the confidentiality of the ML model and the ML program from attack.

Confidential machine learning with program compartmentalization

Systems and methods for implementing a system architecture to support a trusted execution environment (TEE) with computational acceleration are provided. The method includes establishing a first trusted channel between a user application stored on an enclave and a graphics processing unit (GPU) driver loaded on a hypervisor. Establishing the first trusted channel includes leveraging page permissions in an extended page table (EPT) to isolate the first trusted channel between the enclave and the GPU driver in a physical memory of an operating system (OS). The method further includes establishing a second trusted channel between the GPU driver and a GPU device. The method also includes launching a unified TEE that includes the enclave and the hypervisor with execution of application code of the user application.

Graphics processing unit accelerated trusted execution environment

A computer-implemented method for implementing protocol-independent anomaly detection within an industrial control system (ICS) includes implementing a detection stage, including performing byte filtering using a byte filtering model based on at least one new network packet associated with the ICS, performing horizontal detection to determine whether a horizontal constraint anomaly exists in the at least one network packet based on the byte filtering and a horizontal model, including analyzing constraints across different bytes of the at least one new network packet, performing message clustering based on the horizontal detection to generate first cluster information, and performing vertical detection to determine whether a vertical anomaly exists based on the first cluster information and a vertical model, including analyzing a temporal pattern of each byte of the at least one new network packet.

Protocol-independent anomaly detection

Systems and methods for an automotive security gateway include an in-gateway security system that monitors local host behaviors in vehicle devices to identify anomalous local host behaviors using a blueprint model trained to recognize secure local host behaviors. An out-of-gateway security system monitors network traffic across remote hosts, local devices, hotspot network, and in-car network to identify anomalous behaviors using deep packet inspection to inspect packets of the network. A threat mitigation system issues threat mitigation instructions corresponding to the identified anomalous local host behaviors and the anomalous remote host behaviors to secure the vehicle devices by removing the identified anomalous local host behaviors and the anomalous remote host behaviors. Automotive security gateway services and vehicle electronic control units operate the vehicle devices according to the threat mitigation instructions.

Host behavior and network analytics based automotive secure gateway

Methods and systems for performance inference include inferring an internal application status based on a unified call stack trace that includes both user and kernel information by inferring user function instances. A calling context encoding is generated that includes information regarding function calling paths. Application performance is analyzed based on the encoded calling contexts. The analysis includes performing a top-down latency breakdown and ranking calling contexts according to how costly each function calling path is.

Transparent performance inference of whole software layers and context-sensitive performance debugging

Growing community of inventors

Pennington, NJ, United States of America

Chung Hwan Kim