Red Bend Ltd.

Harman International Industries, Inc.

Nir Morgulis

Shachar Mendelowitz

Alexander Kreines

Aashish Kumar

George Jose

Nurit Peres

Sambuddha Saha

Srinivas Kruthiveti Subrahmanyeswara Sai

Disclosed herein are systems and methods for detecting potential malicious attacks in vehicles operational environment using staged Machine Learning (ML), comprising creating a plurality of features vectors each comprising a plurality of features extracted from vehicle operational data generated by a plurality of devices deployed in one or more vehicles which is indicative of operation of the one or more vehicles, detecting, in real-time, a plurality of anomaly feature vectors using one or more unsupervised ML models applied to the plurality of feature vectors, identifying, in real-time, one or more potential cyberattack events using one or more supervised ML models applied to the plurality of anomaly feature vectors, and generating an alert indicative of the one or more potential cyberattack events.

Using staged machine learning to enhance vehicles cybersecurity

Techniques are disclosed for detecting adversarial attacks. A machine learning (ML) system processes the input into and output of a ML model using an adversarial detection module that does not include a direct external interface. The adversarial detection module includes a detection model that generates a score indicative of whether the input is adversarial using, e.g., a neural fingerprinting technique or a comparison of features extracted by a surrogate ML model to an expected feature distribution for the output of the ML model. In turn, the adversarial score is compared to a predefined threshold for raising an adversarial flag. Appropriate remedial measures, such as notifying a user, may be taken when the adversarial score satisfies the threshold and raises the adversarial flag.

Defending machine learning systems from adversarial attacks

Mechanisms and methods are provided for establishing vectors indicating the presence, in a first vehicle and second vehicle, of a super-set of vehicle features present across a fleet of vehicles. The first vehicle may be a reference vehicle. A distance function of the vectors may be calculated in order to establish a similarity score indicating the degree of similarity between the designs of the two vehicles. If the second vehicle is sufficiently similar to the reference vehicle, a software update may be recommended and applied.

Methods for prioritizing updates for vehicle fleets

Growing community of inventors

Petah Tikwa, Israel

Nir Morgulis