Intel Corporation

Other

Apple Inc.

Tahoe Research, Ltd.

Liuyang L Yang

Xiruo Liu

Manoj R Sastry

Marcio Rogerio Juliato

Shabbir Ahmed

Christopher N Gutierrez

Kathiravetpillai Sivanesan

Moreno Ambrosin

Justin E Gottschlich

Vuk Lesi

Kuilin Clark Chen

Sridhar G Sharma

Christian Maciocco

Debabani Choudhury

Yair Yona

Rath Vannithamby

Leonardo Gomes Baltar

Jeffrey Ota

Ned M Smith

Francesc Guim Bernat

Kshitij Arun Doshi

Shilpa Talwar

Satish Chandra Jha

Hosein Nikopour

Ignacio Javier Alvarez

S M Iftekharul Alam

Rafael Misoczki

Li Zhao

Vesh Raj Sharma Banjade

Qian Wang

Minyoung Park

Jing Zhu

Bahareh Sadeghi

Guoqing C Li

Xingang Guo

Mousumi M Hazra

Zheng Zhang

Ralf Graefe

Rafael Rosales

Patrick Mead

Mohammed Karmoose

Markus Dominik Mueck

Nageen Himayat

Alexander F Heinecke

John Charles Weast

Sara S Baghsorkhi

Santosh Ghosh

Vallabhajosyula S Somayazulu

David Israel Gonzalez Aguirre

Jason Martin

Ching-Yu Liao

Mona Vij

Mandar S Joshi

Shengtian Zhou

Yonghong Huang

Maria Soledad Elli

Marc Jalfon

Arvind Merwaday

Mohammad Mejbah Ul Alam

W Steven Conner

Carol Bell

Yuan Yuan

James Tsai

Shahab Layeghi

Samer Taha

Krishna Paul

Noor Abani

Huaxin Li

The present disclosure describe methods, apparatuses, storage media, and systems for a device disposed at an edge of a vehicular communication network or vehicles within a coverage area of the device. The device is to generate a list of vehicle security data to be distributed to vehicles currently within a coverage area of the device, based at least in part on a context related to the vehicles. The device is further to announce, on a control channel communicatively coupling the device and the vehicles, that the list of vehicle security data are available and a service channel to receive the list of vehicle security data. The list of vehicle security data are to be provided to the vehicles via the service channel. Other embodiments may be described and claimed.

Security certificate management and misbehavior vehicle reporting in vehicle-to-everything (V2X) communication

Methods, apparatus, systems and articles of manufacture are disclosed to validate data communicated by a vehicle. An example apparatus an anomaly detector to, in response to data communicated by a vehicle, at least one of compare an estimated speed with a reported speed or compare a location of the vehicle with a reported location. The apparatus including the anomaly detector further to generate an indication of the vehicle in response to the comparison. The apparatus further includes a notifier to discard data sent by the vehicle and notify surrounding vehicles of the data communicated by the vehicle.

Methods and apparatus to validate data communicated by a vehicle

Systems and techniques for misbehavior processing in connected vehicle networks such as a vehicle-to-everything (V2X) communication environment are described herein. A misbehavior report may be received by a local misbehavior agent from a node operating on a vehicle communication network. The local misbehavior agent may be responsible for a geographic area in which the node is located. The misbehavior report may be corroborated using the misbehavior report and evidence of misbehavior of a subject node of the misbehavior report. A revocation recommendation may be generated for the subject node based on the corroboration. The revocation recommendation may be transmitted to a misbehavior authority operating on the vehicle communication network.

Misbehavior processing in connected vehicle networks

A digital twin model is generated for physical nodes of an edge network. The digital twin model includes a digital twin for a physical node of the physical nodes. An error is identified of the physical node or the digital twin for the physical node. The digital twin model is updated to halt communication with the physical node or the digital twin of the physical node. A path is created to another physical node or a digital twin of the another physical node in the digital twin model.

Digital twin framework for next generation networks

The present disclosure is related to vehicle-to-everything (V2X) and Intelligent Transport System (ITS) communications technologies, and in particular, to misbehavior detection and misbehavior reporting services for Collective Perception Messages (CPMs). The misbehavior detection mechanisms include one or more data consistency checks, including a multi-step systematic data consistency check within individual CPMs, across multiple CPMs from the same transmitter, and across multiple CPMs from different transmitters. Potential misbehaviors are reported to a misbehavior authority in one or more misbehavior reports.

Misbehavior detection using data consistency checks for collective perception messages

A non-transitory computer-readable storage medium stores instructions to configure a base station for user equipment (UE) authentication in a wireless network and to cause the base station to perform an operation comprising decoding configuration signaling received from a PHY security function (PSF) of the wireless network. The configuration signaling includes a request for collection of a plurality of signal samples from the UE, the UE authenticated based on successful completion of a first authentication process. A response message is encoded for transmission to the PSF. The response message includes the plurality of UE signal samples. A trained machine learning model received from the PSF is decoded. The trained machine learning model associates the authenticated UE with an RF signature of the UE. The RF signature is based on the plurality of signal samples. A second authentication process of the UE is performed based on the trained model.

Adaptive and hierarchical network authentication framework

An apparatus and system to mitigate non-genuine handovers are described. The handovers include handovers based on fake measurements and handovers to malicious cells. To mitigate these, a mitigation procedure is initiated when excessive handovers are detected. Location information obtained from the UE, estimation of PHY layer properties by the serving and/or target cell, or AI modeling of the best serving cell at the UE location is used to determine whether the handover is valid. If not, the handover is canceled and the UE is stopped from initiating new handovers for a specified time, the UE may be instructed to perform re-authentication with the network, and/or the serving cell recommends to the network authentication entity to revoke the UE authentication. To ensure that the target cell is legitimate, an AI model is used to classify the target cell as known/unknown and the result sent to the network in NAS signaling.

Physical layer techniques to mitigate the handover process vulnerabilities

A computing node includes network interface circuitry and processing circuitry. The processing circuitry assigns available computing resources to a plurality of slice contexts. Each slice context of the plurality includes resource allocations of the available computing resources associated with multiple communication networks. A first portion of the resource allocations is designated as dedicated resources and a second, remaining portion is designated as shared resources. A FAFO event associated with a workload is detected. The workload executes on a network slice instance (NSI) associated with a slice context of a subset of slice contexts. The configuration of the NSI is restored to a pre-FAFO event state based on reconfiguring one or both of the dedicated resources or the shared resources of the slice context based on the resource allocations of at least a second slice context in the subset of slice contexts.

End-to-end network slicing (ENS) from ran to core network for next generation (NG) communications

A computing node to implement a management entity in a CP-based network. The node including processing circuitry configured to encode an inquiry message requesting information on CPS capabilities. Response messages are received from a set of sensing nodes of a plurality of sensing nodes in response to the inquiry message. The response messages include the information on the CPS capabilities of the set of sensing nodes. A notification message indicating selecting of a sensing node as a sensing coordinator is encoded for transmission. Sensed data received in a broadcast message from the sensing coordinator is decoded. The sensed data including data associated with one or more non-V2X capable sensing nodes.

Occupancy grid map computation, V2X complementary sensing, and coordination of cooperative perception data transmission in wireless networks

A computing node includes interface circuitry and processing circuitry. To implement a spectrum harvesting entity in a wireless network configured for crowdsource-based unlicensed spectrum harvesting, the processing circuitry is configured to select a set of crowdsourcing nodes from a plurality of crowdsourcing nodes available in the wireless network. A plurality of spectrum occupancy reports (SORs) is received from the set of crowdsourcing. Each of the plurality of SORs indicates a channel occupancy status of a communication channel in the unlicensed spectrum sensed by a corresponding crowdsourcing node of the set and geolocation associated with the corresponding crowdsourcing node. A spectrum occupancy map of the unlicensed spectrum is generated based on the channel occupancy status and the geolocation provided in the plurality of SORs by each crowdsourcing node of the set of crowdsourcing nodes.

Unlicensed spectrum harvesting with collaborative spectrum sensing in next generation networks

Methods and apparatus relating to a physics-based approach for attack detection and/or localization in closed-loop controls for autonomous vehicles are described. In an embodiment, multiple state estimators are used to compute a set of residuals to detect, classify, and/or localize attacks. This allows for determination of an attacker's location and the kind of attack being perpetrated. Other embodiments are also disclosed and claimed.

Physics-based approach for attack detection and localization in closed-loop controls for autonomous vehicles

Logic may implement observation layer intrusion detection systems (IDSs) to combine observations by intrusion detectors and/or other intrusion detection systems. Logic may monitor one or more control units at one or more observation layers of an in-vehicle network, each of the one or more control units to perform a vehicle function. Logic may combine observations of the one or more control units at the one or more observation layers. Logic may determine, based on a combination of the observations, that one or more of the observations represent an intrusion. Logic may determine, based at least on the observations, characteristics of an attack, and to pass the characteristics of the attack information to a forensic logging system to log the attack or pass the characteristics of the attack to a recovery system for informed selection of recovery procedures. Logic may dynamically adjust a threshold for detection of suspicious activity.

Methods and arrangements for multi-layer in-vehicle network intrusion detection and characterization

Various embodiments are generally directed to providing authentication and confidentiality mechanisms for message communication over an in-vehicle network. For example, authentication data associated with a communicating node may be transmitted over the network by encoding different predefined voltage levels on top of the message bits of the message being communicated. Different voltage levels may represent different encodings, such as a bit-pair or any bit combination of the authentication data. In a further example, messaging confidentiality between at least two communicating nodes may be achieved by pseudo-randomly flipping, or scrambling, the dominant and recessive voltages of the entire message frame at the analog level based on a pseudo-random control bit sequence.

Voltage encoded MAC and bus scrambling

Systems, apparatus, methods, and techniques for reporting an attack or intrusion into an in-vehicle network are provided. The attack can be broadcast to connected vehicles over a vehicle-to-vehicle network. The broadcast can include an indication of a sub-system involved in the attack and can include a request for assistance in recovering from the attack. Connected vehicles can broadcast responses over the vehicle-to-vehicle network. The responses can include indications of data related to the compromised sub-system. The vehicle can receive the responses and can use the responses to recover from the attack, such as, estimate data.

Recover from vehicle security breach via vehicle to anything communication

Systems, apparatus, methods, and techniques for an ego vehicle to respond to detecting misbehaving information from remote vehicles are provided. An ego vehicle, in addition to reporting misbehaving vehicles to a misbehavior authority via a vehicle-to-anything communication network, can, take additional actions based in part on how confident the ego vehicle is about the evidence of misbehavior. Where the confidence is high the ego vehicle can simply discard the misbehaving data and provide an alternative estimate for such data from alternative sources. Where the confidence is not high the ego vehicle can request assistance from neighboring vehicles and roadside units to provide independent estimates of the data to increase confidence in the evidence of misbehavior.

Ego actions in response to misbehaving vehicle identification

Growing community of inventors

Portland, OR, United States of America

Liuyang L Yang