{
  "$type": "site.standard.document",
  "description": "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…",
  "path": "/patents/1333653",
  "publishedAt": "2022-12-15T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "H04L63/1416",
    "Red Bend Ltd."
  ],
  "textContent": "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.",
  "title": "USING STAGED MACHINE LEARNING TO ENHANCE VEHICLES CYBERSECURITY"
}