Securing PKES: Integrating Artificial Intelligence into PKES Forcefield for Anomaly Detection

Syed Rizvi, Jarrett Imler, Luke Ritchey, Michael Tokar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Passive Keyless Entry and Start System (PKES) is a convenient feature found in newer cars that allows the user to enter their car without having to press a button on a key fob. Users can simply walk up to their vehicle and once they are in close enough proximity, it unlocks and the user is granted access. However, the convenience of PKES comes at a price; vulnerability to relay attacks and amplified relay attacks. Our proposed method, PKES Forcefield, would protect the user from these attacks without sacrificing any convenience. PKES Forcefield utilizes coordinate tracing and multifactor authentication to secure the user. To add more security to PKES Forcefield, we propose AI integration to construct a profile for each user of the vehicle. This profile will store data on the habits of the user (location, walking speed, time, etc.). Once each user has a profile built from their input data, our system will be able to detect any behaviors that differ from the user's profile (anomalies) and then proper security measures will be taken depending on if the anomaly is considered to be a Low Risk Anomaly or a High Risk Anomaly.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 International Conference on Software Security and Assurance, ICSSA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages10-15
Number of pages6
ISBN (Electronic)9781728159126
DOIs
StatePublished - Jul 2019
Event5th International Conference on Software Security and Assurance, ICSSA 2019 - St. Polten, Austria
Duration: Jul 25 2019Jul 26 2019

Publication series

NameProceedings - 2019 International Conference on Software Security and Assurance, ICSSA 2019

Conference

Conference5th International Conference on Software Security and Assurance, ICSSA 2019
CountryAustria
CitySt. Polten
Period7/25/197/26/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Safety, Risk, Reliability and Quality

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